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https://aclanthology.org/2024.acl-long.1.bib | @inproceedings{zhang-etal-2024-quantized,
title = "Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models",
author = "Zhang, Zhengxin and
Zhao, Dan and
Miao, Xupeng and
Oliaro, Gabriele and
Zhang, Zhihao and
Li, Qing and
Jiang, Yong and
Jia, Zhihao",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.1",
pages = "1--17",
abstract = "Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficient finetuning, which only updates a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetuning. Typically, the memory footprint during finetuning stems from three contributors: model weights, optimizer states, and intermediate activations. However, existing works still require considerable memory, and none can simultaneously mitigate the memory footprint of all three sources. In this paper, we present quantized side tuing (QST), which enables memory-efficient and fast finetuning of LLMs by operating through a dual-stage process. First, QST quantizes an LLM{'}s model weights into 4-bit to reduce the memory footprint of the LLM{'}s original weights. Second, QST introduces a side network separated from the LLM, which utilizes the hidden states of the LLM to make task-specific predictions. Using a separate side network avoids performing back-propagation through the LLM, thus reducing the memory requirement of the intermediate activations. Finally, QST leverages several low-rank adaptors and gradient-free downsample modules to significantly reduce the trainable parameters, so as to save the memory footprint of the optimizer states. Experiments show that QST can reduce the total memory footprint by up to 2.3{\mbox{$\times$}} and speed up the finetuning process by up to 3$\times$ while achieving competent performance compared with the state-of-the-art. When it comes to full finetuning, QST can reduce the total memory footprint up to 7$\times$.",
}
| Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficient finetuning, which only updates a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetuning. Typically, the memory footprint during finetuning stems from three contributors: model weights, optimizer states, and intermediate activations. However, existing works still require considerable memory, and none can simultaneously mitigate the memory footprint of all three sources. In this paper, we present quantized side tuing (QST), which enables memory-efficient and fast finetuning of LLMs by operating through a dual-stage process. First, QST quantizes an LLM{'}s model weights into 4-bit to reduce the memory footprint of the LLM{'}s original weights. Second, QST introduces a side network separated from the LLM, which utilizes the hidden states of the LLM to make task-specific predictions. Using a separate side network avoids performing back-propagation through the LLM, thus reducing the memory requirement of the intermediate activations. Finally, QST leverages several low-rank adaptors and gradient-free downsample modules to significantly reduce the trainable parameters, so as to save the memory footprint of the optimizer states. Experiments show that QST can reduce the total memory footprint by up to 2.3{\mbox{$\times$}} and speed up the finetuning process by up to 3$\times$ while achieving competent performance compared with the state-of-the-art. When it comes to full finetuning, QST can reduce the total memory footprint up to 7$\times$. | [
"Zhang, Zhengxin",
"Zhao, Dan",
"Miao, Xupeng",
"Oliaro, Gabriele",
"Zhang, Zhihao",
"Li, Qing",
"Jiang, Yong",
"Jia, Zhihao"
] | Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models | acl-long.1 | Oral | 2401.07159 | [
"https://github.com/youarespecialtome/qst"
] | https://huggingface.co/papers/2401.07159 | 2 | 0 | 0 | 7 | https://aclanthology.org/2024.acl-long.1/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.2.bib | @inproceedings{zhang-etal-2024-unsupervised,
title = "Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances",
author = "Zhang, Hanlei and
Xu, Hua and
Long, Fei and
Wang, Xin and
Gao, Kai",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.2",
pages = "18--35",
abstract = "Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample{'}s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-$K$ parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6{\%} scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://github.com/thuiar/UMC.",
}
| Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample{'}s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-$K$ parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6{\%} scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://github.com/thuiar/UMC. | [
"Zhang, Hanlei",
"Xu, Hua",
"Long, Fei",
"Wang, Xin",
"Gao, Kai"
] | Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances | acl-long.2 | Poster | 2405.12775 | [
"https://github.com/thuiar/umc"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.2/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.3.bib | @inproceedings{li-etal-2024-mage,
title = "{MAGE}: Machine-generated Text Detection in the Wild",
author = "Li, Yafu and
Li, Qintong and
Cui, Leyang and
Bi, Wei and
Wang, Zhilin and
Wang, Longyue and
Yang, Linyi and
Shi, Shuming and
Zhang, Yue",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.3",
pages = "36--53",
abstract = "Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective deepfake text detection to mitigate risks like the spread of fake news and plagiarism. Existing research has been constrained by evaluating detection methods o specific domains or particular language models. In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources. To this end, we build a comprehensive testbed by gathering texts from diverse human writings and deepfake texts generated by different LLMs. Empirical results on mainstream detection methods demonstrate the difficulties associated with detecting deepfake text in a wide-ranging testbed, particularly in out-of-distribution scenarios. Such difficulties align with the diminishing linguistic differences between the two text sources. Despite challenges, the top-performing detector can identify 84.12{\%} out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.",
}
| Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective deepfake text detection to mitigate risks like the spread of fake news and plagiarism. Existing research has been constrained by evaluating detection methods o specific domains or particular language models. In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources. To this end, we build a comprehensive testbed by gathering texts from diverse human writings and deepfake texts generated by different LLMs. Empirical results on mainstream detection methods demonstrate the difficulties associated with detecting deepfake text in a wide-ranging testbed, particularly in out-of-distribution scenarios. Such difficulties align with the diminishing linguistic differences between the two text sources. Despite challenges, the top-performing detector can identify 84.12{\%} out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios. | [
"Li, Yafu",
"Li, Qintong",
"Cui, Leyang",
"Bi, Wei",
"Wang, Zhilin",
"Wang, Longyue",
"Yang, Linyi",
"Shi, Shuming",
"Zhang, Yue"
] | MAGE: Machine-generated Text Detection in the Wild | acl-long.3 | Poster | 2305.13242 | [
"https://github.com/yafuly/deepfaketextdetect"
] | https://huggingface.co/papers/2305.13242 | 2 | 0 | 1 | 8 | https://aclanthology.org/2024.acl-long.3/ | [
"yaful/MAGE"
] | [
"yaful/MAGE"
] | [
"yaful/DeepfakeTextDetect"
] | 1 |
https://aclanthology.org/2024.acl-long.4.bib | @inproceedings{li-etal-2024-privlm,
title = "{P}riv{LM}-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models",
author = "Li, Haoran and
Guo, Dadi and
Li, Donghao and
Fan, Wei and
Hu, Qi and
Liu, Xin and
Chan, Chunkit and
Yao, Duanyi and
Yao, Yuan and
Song, Yangqiu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.4",
pages = "54--73",
abstract = "The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs achieve state-of-the-art performance over numerous downstream NLP tasks. On the other hand, more and more attention is paid to unrestricted model accesses that may bring malicious privacy risks of data leakage. To address these issues, many recent works propose privacy-preserving language models (PPLMs) with differential privacy (DP). Unfortunately, different DP implementations make it challenging for a fair comparison among existing PPLMs. In this paper, we present PrivLM-Bench, a multi-perspective privacy evaluation benchmark to empirically and intuitively quantify the privacy leakage of LMs. Instead of only reporting DP parameters, PrivLM-Bench sheds light on the neglected inference data privacy during actual usage. PrivLM-Bench first clearly defines multi-faceted privacy objectives. Then, PrivLM-Bench constructs a unified pipeline to perform private fine-tuning. Lastly, PrivLM-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results. The empirical attack results are used to fairly and intuitively evaluate the privacy leakage of various PPLMs. We conduct extensive experiments on three datasets of GLUE for mainstream LMs.",
}
| The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs achieve state-of-the-art performance over numerous downstream NLP tasks. On the other hand, more and more attention is paid to unrestricted model accesses that may bring malicious privacy risks of data leakage. To address these issues, many recent works propose privacy-preserving language models (PPLMs) with differential privacy (DP). Unfortunately, different DP implementations make it challenging for a fair comparison among existing PPLMs. In this paper, we present PrivLM-Bench, a multi-perspective privacy evaluation benchmark to empirically and intuitively quantify the privacy leakage of LMs. Instead of only reporting DP parameters, PrivLM-Bench sheds light on the neglected inference data privacy during actual usage. PrivLM-Bench first clearly defines multi-faceted privacy objectives. Then, PrivLM-Bench constructs a unified pipeline to perform private fine-tuning. Lastly, PrivLM-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results. The empirical attack results are used to fairly and intuitively evaluate the privacy leakage of various PPLMs. We conduct extensive experiments on three datasets of GLUE for mainstream LMs. | [
"Li, Haoran",
"Guo, Dadi",
"Li, Donghao",
"Fan, Wei",
"Hu, Qi",
"Liu, Xin",
"Chan, Chunkit",
"Yao, Duanyi",
"Yao, Yuan",
"Song, Yangqiu"
] | PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models | acl-long.4 | Oral | 2311.04044 | [
"https://github.com/hkust-knowcomp/privlm-bench"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.4/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.5.bib | @inproceedings{hu-etal-2024-gentranslate,
title = "{G}en{T}ranslate: Large Language Models are Generative Multilingual Speech and Machine Translators",
author = "Hu, Yuchen and
Chen, Chen and
Yang, Chao-Han and
Li, Ruizhe and
Zhang, Dong and
Chen, Zhehuai and
Chng, EngSiong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.5",
pages = "74--90",
abstract = "Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully exploit the rich information in the diverse N-best hypotheses, making them less optimal for translation tasks that require a single, high-quality output sequence. In this paper, we propose a new generative paradigm for translation tasks, namely GenTranslate, which builds upon LLMs to generate better results from the diverse translation versions in N-best list. Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the diverse N-best candidates to generate a higher-quality translation result. Furthermore, to support LLM finetuning, we build and release a HypoTranslate dataset that contains over 592K hypotheses-translation pairs in 11 languages. Experiments on various speech and machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that our GenTranslate significantly outperforms the state-of-the-art model.",
}
| Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully exploit the rich information in the diverse N-best hypotheses, making them less optimal for translation tasks that require a single, high-quality output sequence. In this paper, we propose a new generative paradigm for translation tasks, namely GenTranslate, which builds upon LLMs to generate better results from the diverse translation versions in N-best list. Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the diverse N-best candidates to generate a higher-quality translation result. Furthermore, to support LLM finetuning, we build and release a HypoTranslate dataset that contains over 592K hypotheses-translation pairs in 11 languages. Experiments on various speech and machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that our GenTranslate significantly outperforms the state-of-the-art model. | [
"Hu, Yuchen",
"Chen, Chen",
"Yang, Chao-Han",
"Li, Ruizhe",
"Zhang, Dong",
"Chen, Zhehuai",
"Chng, EngSiong"
] | GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators | acl-long.5 | Oral | 2402.06894 | [
"https://github.com/yuchen005/gentranslate"
] | https://huggingface.co/papers/2402.06894 | 1 | 0 | 0 | 7 | https://aclanthology.org/2024.acl-long.5/ | [
"PeacefulData/GenTranslate"
] | [
"PeacefulData/HypoTranslate"
] | [] | 1 |
https://aclanthology.org/2024.acl-long.6.bib | @inproceedings{xu-etal-2024-exploring,
title = "Exploring Chain-of-Thought for Multi-modal Metaphor Detection",
author = "Xu, Yanzhi and
Hua, Yueying and
Li, Shichen and
Wang, Zhongqing",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.6",
pages = "91--101",
abstract = "Metaphors are commonly found in advertising and internet memes. However, the free form of internet memes often leads to a lack of high-quality textual data. Metaphor detection demands a deep interpretation of both textual and visual elements, requiring extensive common-sense knowledge, which poses a challenge to language models. To address these challenges, we propose a compact framework called C4MMD, which utilizes a \textbf{C}hain-of-Thought(CoT) method \textbf{for} \textbf{M}ulti-modal \textbf{M}etaphor \textbf{D}etection. Specifically, our approach designs a three-step process inspired by CoT that extracts and integrates knowledge from Multi-modal Large Language Models(MLLMs) into smaller ones. We also developed a modality fusion architecture to transform knowledge from large models into metaphor features, supplemented by auxiliary tasks to improve model performance. Experimental results on the MET-MEME dataset demonstrate that our method not only effectively enhances the metaphor detection capabilities of small models but also outperforms existing models. To our knowledge, this is the first systematic study leveraging MLLMs in metaphor detection tasks. The code for our method is publicly available at \url{https://github.com/xyz189411yt/C4MMD}.",
}
| Metaphors are commonly found in advertising and internet memes. However, the free form of internet memes often leads to a lack of high-quality textual data. Metaphor detection demands a deep interpretation of both textual and visual elements, requiring extensive common-sense knowledge, which poses a challenge to language models. To address these challenges, we propose a compact framework called C4MMD, which utilizes a \textbf{C}hain-of-Thought(CoT) method \textbf{for} \textbf{M}ulti-modal \textbf{M}etaphor \textbf{D}etection. Specifically, our approach designs a three-step process inspired by CoT that extracts and integrates knowledge from Multi-modal Large Language Models(MLLMs) into smaller ones. We also developed a modality fusion architecture to transform knowledge from large models into metaphor features, supplemented by auxiliary tasks to improve model performance. Experimental results on the MET-MEME dataset demonstrate that our method not only effectively enhances the metaphor detection capabilities of small models but also outperforms existing models. To our knowledge, this is the first systematic study leveraging MLLMs in metaphor detection tasks. The code for our method is publicly available at \url{https://github.com/xyz189411yt/C4MMD}. | [
"Xu, Yanzhi",
"Hua, Yueying",
"Li, Shichen",
"Wang, Zhongqing"
] | Exploring Chain-of-Thought for Multi-modal Metaphor Detection | acl-long.6 | Poster | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.6/ | [] | [] | [] | 0 |
||
https://aclanthology.org/2024.acl-long.7.bib | @inproceedings{du-etal-2024-bitdistiller,
title = "{B}it{D}istiller: Unleashing the Potential of Sub-4-Bit {LLM}s via Self-Distillation",
author = "Du, DaYou and
Zhang, Yijia and
Cao, Shijie and
Guo, Jiaqi and
Cao, Ting and
Chu, Xiaowen and
Xu, Ningyi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.7",
pages = "102--116",
abstract = "The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistiller, a framework that synergizes Quantization-Aware Training (QAT) with Knowledge Distillation (KD) to boost the performance of LLMs at ultra-low precisions (sub-4-bit). Specifically, BitDistiller first incorporates a tailored asymmetric quantization and clipping technique to maximally preserve the fidelity of quantized weights, and then proposes a novel Confidence-Aware Kullback-Leibler Divergence (CAKLD) objective, which is employed in a self-distillation manner to enable faster convergence and superior model performance. Empirical evaluations demonstrate that BitDistiller significantly surpasses existing methods in both 3-bit and 2-bit configurations on general language understanding and complex reasoning benchmarks. Notably, BitDistiller is shown to be more cost-effective, demanding fewer data and training resources. The code is available at https://github.com/DD-DuDa/BitDistiller.",
}
| The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistiller, a framework that synergizes Quantization-Aware Training (QAT) with Knowledge Distillation (KD) to boost the performance of LLMs at ultra-low precisions (sub-4-bit). Specifically, BitDistiller first incorporates a tailored asymmetric quantization and clipping technique to maximally preserve the fidelity of quantized weights, and then proposes a novel Confidence-Aware Kullback-Leibler Divergence (CAKLD) objective, which is employed in a self-distillation manner to enable faster convergence and superior model performance. Empirical evaluations demonstrate that BitDistiller significantly surpasses existing methods in both 3-bit and 2-bit configurations on general language understanding and complex reasoning benchmarks. Notably, BitDistiller is shown to be more cost-effective, demanding fewer data and training resources. The code is available at https://github.com/DD-DuDa/BitDistiller. | [
"Du, DaYou",
"Zhang, Yijia",
"Cao, Shijie",
"Guo, Jiaqi",
"Cao, Ting",
"Chu, Xiaowen",
"Xu, Ningyi"
] | BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation | acl-long.7 | Poster | 2402.10631 | [
"https://github.com/dd-duda/bitdistiller"
] | https://huggingface.co/papers/2402.10631 | 1 | 1 | 0 | 7 | https://aclanthology.org/2024.acl-long.7/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.8.bib | @inproceedings{chen-etal-2024-unified,
title = "A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation",
author = "Chen, Kai and
Wang, Ye and
Li, Yitong and
Li, Aiping and
Yu, Han and
Song, Xin",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.8",
pages = "117--132",
abstract = "Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chronological order of knowledge and ignore inferring clues provided by missing facts of the past. These limit the practicability of TKG applications as almost all of the existing TKG reasoning methods are designed specifically to address either one setting. To this end, this paper proposes an original Temporal PAth-based Reasoning (TPAR) model for both the interpolation and extrapolation reasoning settings. TPAR performs a neural-driven symbolic reasoning fashion that is robust to ambiguous and noisy temporal data, and with fine interpretability as well. Comprehensive experiments show that TPAR outperforms SOTA methods on the link prediction task for both the interpolation and the extrapolation settings. A novel pipeline experimental setting is designed to evaluate the performances of SOTA combinations and the proposed TPAR towards interpolation and extrapolation reasoning. And more diverse experiments are conducted to show the robustness and interpretability of TPAR.",
}
| Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chronological order of knowledge and ignore inferring clues provided by missing facts of the past. These limit the practicability of TKG applications as almost all of the existing TKG reasoning methods are designed specifically to address either one setting. To this end, this paper proposes an original Temporal PAth-based Reasoning (TPAR) model for both the interpolation and extrapolation reasoning settings. TPAR performs a neural-driven symbolic reasoning fashion that is robust to ambiguous and noisy temporal data, and with fine interpretability as well. Comprehensive experiments show that TPAR outperforms SOTA methods on the link prediction task for both the interpolation and the extrapolation settings. A novel pipeline experimental setting is designed to evaluate the performances of SOTA combinations and the proposed TPAR towards interpolation and extrapolation reasoning. And more diverse experiments are conducted to show the robustness and interpretability of TPAR. | [
"Chen, Kai",
"Wang, Ye",
"Li, Yitong",
"Li, Aiping",
"Yu, Han",
"Song, Xin"
] | A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation | acl-long.8 | Poster | 2405.18106 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.8/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.9.bib | @inproceedings{xu-etal-2024-unsupervised,
title = "Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation",
author = "Xu, Shicheng and
Pang, Liang and
Yu, Mo and
Meng, Fandong and
Shen, Huawei and
Cheng, Xueqi and
Zhou, Jie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.9",
pages = "133--145",
abstract = "Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face challenges in effectively using the retrieved information, even ignore it or be misled by it. The key reason is that the training of LLMs does not clearly make LLMs learn how to utilize input retrieved texts with varied quality. In this paper, we propose a novel perspective that considers the role of LLMs in RAG as {``}Information Refiner{''}, which means that regardless of correctness, completeness, or usefulness of retrieved texts, LLMs can consistently integrate knowledge within the retrieved texts and model parameters to generate the texts that are more concise, accurate, and complete than the retrieved texts. To this end, we propose an information refinement training method named INFO-RAG that optimizes LLMs for RAG in an unsupervised manner. INFO-RAG is low-cost and general across various tasks. Extensive experiments on zero-shot prediction of 11 datasets in diverse tasks including Question Answering, Slot-Filling, Language Modeling, Dialogue, and Code Generation show that INFO-RAG improves the performance of LLaMA2 by an average of 9.39{\%} relative points. INFO-RAG also shows advantages in in-context learning and robustness of RAG.",
}
| Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face challenges in effectively using the retrieved information, even ignore it or be misled by it. The key reason is that the training of LLMs does not clearly make LLMs learn how to utilize input retrieved texts with varied quality. In this paper, we propose a novel perspective that considers the role of LLMs in RAG as {``}Information Refiner{''}, which means that regardless of correctness, completeness, or usefulness of retrieved texts, LLMs can consistently integrate knowledge within the retrieved texts and model parameters to generate the texts that are more concise, accurate, and complete than the retrieved texts. To this end, we propose an information refinement training method named INFO-RAG that optimizes LLMs for RAG in an unsupervised manner. INFO-RAG is low-cost and general across various tasks. Extensive experiments on zero-shot prediction of 11 datasets in diverse tasks including Question Answering, Slot-Filling, Language Modeling, Dialogue, and Code Generation show that INFO-RAG improves the performance of LLaMA2 by an average of 9.39{\%} relative points. INFO-RAG also shows advantages in in-context learning and robustness of RAG. | [
"Xu, Shicheng",
"Pang, Liang",
"Yu, Mo",
"Meng, F",
"ong",
"Shen, Huawei",
"Cheng, Xueqi",
"Zhou, Jie"
] | Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation | acl-long.9 | Poster | 2402.18150 | [
"https://github.com/xsc1234/info-rag"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.9/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.10.bib | @inproceedings{hu-etal-2024-cscd,
title = "{CSCD}-{NS}: a {C}hinese Spelling Check Dataset for Native Speakers",
author = "Hu, Yong and
Meng, Fandong and
Zhou, Jie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.10",
pages = "146--159",
abstract = "In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a significantly higher proportion of word-level errors. To further enhance the data resource, we propose a novel method that simulates the input process through an input method, generating large-scale and high-quality pseudo data that closely resembles the actual error distribution and outperforms existing methods. Moreover, we investigate the performance of various models in this scenario, including large language models (LLMs), such as ChatGPT. The result indicates that generative models underperform BERT-like classification models due to strict length and pronunciation constraints. The high prevalence of word-level errors also makes CSC for native speakers challenging enough, leaving substantial room for improvement.",
}
| In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a significantly higher proportion of word-level errors. To further enhance the data resource, we propose a novel method that simulates the input process through an input method, generating large-scale and high-quality pseudo data that closely resembles the actual error distribution and outperforms existing methods. Moreover, we investigate the performance of various models in this scenario, including large language models (LLMs), such as ChatGPT. The result indicates that generative models underperform BERT-like classification models due to strict length and pronunciation constraints. The high prevalence of word-level errors also makes CSC for native speakers challenging enough, leaving substantial room for improvement. | [
"Hu, Yong",
"Meng, F",
"ong",
"Zhou, Jie"
] | CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers | acl-long.10 | Poster | 2211.08788 | [
"https://github.com/nghuyong/cscd-ime"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.10/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.11.bib | @inproceedings{karakkaparambil-james-etal-2024-evaluating,
title = "Evaluating Dynamic Topic Models",
author = "Karakkaparambil James, Charu and
Nagda, Mayank and
Haji Ghassemi, Nooshin and
Kloft, Marius and
Fellenz, Sophie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.11",
pages = "160--176",
abstract = "There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality with the model{'}s temporal consistency. We demonstrate the utility of the proposed measure by applying it to synthetic data and data from existing DTMs, including DTMs from large language models (LLMs). We also show that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs and LLMs, and guiding future research in this area.",
}
| There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality with the model{'}s temporal consistency. We demonstrate the utility of the proposed measure by applying it to synthetic data and data from existing DTMs, including DTMs from large language models (LLMs). We also show that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs and LLMs, and guiding future research in this area. | [
"Karakkaparambil James, Charu",
"Nagda, Mayank",
"Haji Ghassemi, Nooshin",
"Kloft, Marius",
"Fellenz, Sophie"
] | Evaluating Dynamic Topic Models | acl-long.11 | Poster | 2309.08627 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.11/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.12.bib | @inproceedings{dong-etal-2024-abilities,
title = "How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition",
author = "Dong, Guanting and
Yuan, Hongyi and
Lu, Keming and
Li, Chengpeng and
Xue, Mingfeng and
Liu, Dayiheng and
Wang, Wei and
Yuan, Zheng and
Zhou, Chang and
Zhou, Jingren",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.12",
pages = "177--198",
abstract = "Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, codegeneration, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT for enhancing individual capabilities, proprietary LLMs exhibit versatility across various skills. Therefore, understanding the facilitation of multiple abilities via SFT is paramount. In this study, we specificially focuses on the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during SFT. We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies. Our experiments reveal that distinct capabilities scale differently and larger models generally show superior performance with same amount of data. Mathematical reasoning and code generation consistently improve with increasing data amount, whereas general abilities plateau after roughly a thousand samples. Moreover, we observe data composition appears to enhance various abilities under limited data conditions, yet can lead to performance conflicts when data is plentiful. Our findings also suggest the amount of composition data influences performance more than the composition ratio. In analysis of SFT strategies, we find that sequentially learning multiple skills risks catastrophic forgetting. Our proposed Dual-stage Mixed Fine-tuning (DMT) strategy offers a promising solution to learn multiple abilities with different scaling patterns.",
}
| Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, codegeneration, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT for enhancing individual capabilities, proprietary LLMs exhibit versatility across various skills. Therefore, understanding the facilitation of multiple abilities via SFT is paramount. In this study, we specificially focuses on the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during SFT. We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies. Our experiments reveal that distinct capabilities scale differently and larger models generally show superior performance with same amount of data. Mathematical reasoning and code generation consistently improve with increasing data amount, whereas general abilities plateau after roughly a thousand samples. Moreover, we observe data composition appears to enhance various abilities under limited data conditions, yet can lead to performance conflicts when data is plentiful. Our findings also suggest the amount of composition data influences performance more than the composition ratio. In analysis of SFT strategies, we find that sequentially learning multiple skills risks catastrophic forgetting. Our proposed Dual-stage Mixed Fine-tuning (DMT) strategy offers a promising solution to learn multiple abilities with different scaling patterns. | [
"Dong, Guanting",
"Yuan, Hongyi",
"Lu, Keming",
"Li, Chengpeng",
"Xue, Mingfeng",
"Liu, Dayiheng",
"Wang, Wei",
"Yuan, Zheng",
"Zhou, Chang",
"Zhou, Jingren"
] | How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition | acl-long.12 | Poster | 2310.05492 | [
"https://github.com/ofa-sys/gsm8k-screl"
] | https://huggingface.co/papers/2310.05492 | 3 | 2 | 0 | 10 | https://aclanthology.org/2024.acl-long.12/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.13.bib | @inproceedings{xu-etal-2024-lens,
title = "Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification",
author = "Xu, Shanshan and
T.y.s.s, Santosh and
Ichim, Oana and
Plank, Barbara and
Grabmair, Matthias",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.13",
pages = "199--216",
abstract = "In legal decisions, split votes (SV) occur when judges cannot reach a unanimous decision, posing a difficulty for lawyers who must navigate diverse legal arguments and opinions. In high-stakes domains, {\%}as human-AI interaction systems become increasingly important, understanding the alignment of perceived difficulty between humans and AI systems is crucial to build trust. However, existing NLP calibration methods focus on a classifier{'}s awareness of predictive performance, measured against the human majority class, overlooking inherent human label variation (HLV). This paper explores split votes as naturally observable human disagreement and value pluralism. We collect judges{'} vote distributions from the European Court of Human Rights (ECHR), and present SV-ECHR, a case outcome classification (COC) dataset with SV information. We build a taxonomy of disagreement with SV-specific subcategories. We further assess the alignment of perceived difficulty between models and humans, as well as confidence- and human-calibration of COC models. We observe limited alignment with the judge vote distribution. To our knowledge, this is the first systematic exploration of calibration to human judgements in legal NLP. Our study underscores the necessity for further research on measuring and enhancing model calibration considering HLV in legal decision tasks.",
}
| In legal decisions, split votes (SV) occur when judges cannot reach a unanimous decision, posing a difficulty for lawyers who must navigate diverse legal arguments and opinions. In high-stakes domains, {\%}as human-AI interaction systems become increasingly important, understanding the alignment of perceived difficulty between humans and AI systems is crucial to build trust. However, existing NLP calibration methods focus on a classifier{'}s awareness of predictive performance, measured against the human majority class, overlooking inherent human label variation (HLV). This paper explores split votes as naturally observable human disagreement and value pluralism. We collect judges{'} vote distributions from the European Court of Human Rights (ECHR), and present SV-ECHR, a case outcome classification (COC) dataset with SV information. We build a taxonomy of disagreement with SV-specific subcategories. We further assess the alignment of perceived difficulty between models and humans, as well as confidence- and human-calibration of COC models. We observe limited alignment with the judge vote distribution. To our knowledge, this is the first systematic exploration of calibration to human judgements in legal NLP. Our study underscores the necessity for further research on measuring and enhancing model calibration considering HLV in legal decision tasks. | [
"Xu, Shanshan",
"T.y.s.s, Santosh",
"Ichim, Oana",
"Plank, Barbara",
"Grabmair, Matthias"
] | Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification | acl-long.13 | Oral | 2402.07214 | [
""
] | https://huggingface.co/papers/2402.07214 | 0 | 0 | 0 | 5 | https://aclanthology.org/2024.acl-long.13/ | [] | [
"sxu/SV-ECHR"
] | [] | 1 |
https://aclanthology.org/2024.acl-long.14.bib | @inproceedings{dalal-etal-2024-inference,
title = "Inference to the Best Explanation in Large Language Models",
author = "Dalal, Dhairya and
Valentino, Marco and
Freitas, Andre and
Buitelaar, Paul",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.14",
pages = "217--235",
abstract = "While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes \textit{IBE-Eval}, a framework inspired by philosophical accounts on \textit{Inference to the Best Explanation (IBE)} to advance the interpretation and evaluation of LLMs{'} explanations. \textit{IBE-Eval} estimates the plausibility of natural language explanations through a combination of explicit logical and linguistic features including: \textit{consistency}, \textit{parsimony}, \textit{coherence}, and \textit{uncertainty}. Extensive experiments are conducted on \textit{Causal Question Answering (CQA)}, where \textit{IBE-Eval} is tasked to select the most plausible causal explanation amongst competing ones generated by LLMs (i.e., GPT 3.5 and Llama 2). The experiments reveal that \textit{IBE-Eval} can successfully identify the best explanation with up to 77{\%} accuracy ($\approx 27\%$ above random), improving upon a GPT 3.5-as-a-Judge baseline ($\approx+17\%$) while being intrinsically more efficient and interpretable. Additional analyses suggest that, despite model-specific variances, LLM-generated explanations tend to conform to IBE criteria and that \textit{IBE-Eval} is significantly correlated with human judgment, opening up opportunities for future development of automated explanation verification tools.",
}
| While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes \textit{IBE-Eval}, a framework inspired by philosophical accounts on \textit{Inference to the Best Explanation (IBE)} to advance the interpretation and evaluation of LLMs{'} explanations. \textit{IBE-Eval} estimates the plausibility of natural language explanations through a combination of explicit logical and linguistic features including: \textit{consistency}, \textit{parsimony}, \textit{coherence}, and \textit{uncertainty}. Extensive experiments are conducted on \textit{Causal Question Answering (CQA)}, where \textit{IBE-Eval} is tasked to select the most plausible causal explanation amongst competing ones generated by LLMs (i.e., GPT 3.5 and Llama 2). The experiments reveal that \textit{IBE-Eval} can successfully identify the best explanation with up to 77{\%} accuracy ($\approx 27\%$ above random), improving upon a GPT 3.5-as-a-Judge baseline ($\approx+17\%$) while being intrinsically more efficient and interpretable. Additional analyses suggest that, despite model-specific variances, LLM-generated explanations tend to conform to IBE criteria and that \textit{IBE-Eval} is significantly correlated with human judgment, opening up opportunities for future development of automated explanation verification tools. | [
"Dalal, Dhairya",
"Valentino, Marco",
"Freitas, Andre",
"Buitelaar, Paul"
] | Inference to the Best Explanation in Large Language Models | acl-long.14 | Poster | 2402.10767 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.14/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.15.bib | @inproceedings{poesina-etal-2024-novel,
title = "A Novel Cartography-Based Curriculum Learning Method Applied on {R}o{NLI}: The First {R}omanian Natural Language Inference Corpus",
author = "Poesina, Eduard and
Caragea, Cornelia and
Ionescu, Radu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.15",
pages = "236--253",
abstract = "Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledge, there is no publicly available NLI corpus for the Romanian language. To this end, we introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels. We conduct experiments with multiple machine learning methods based on distant learning, ranging from shallow models based on word embeddings to transformer-based neural networks, to establish a set of competitive baselines. Furthermore, we improve on the best model by employing a new curriculum learning strategy based on data cartography. Our dataset and code to reproduce the baselines are available at https://github.com/Eduard6421/RONLI.",
}
| Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledge, there is no publicly available NLI corpus for the Romanian language. To this end, we introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels. We conduct experiments with multiple machine learning methods based on distant learning, ranging from shallow models based on word embeddings to transformer-based neural networks, to establish a set of competitive baselines. Furthermore, we improve on the best model by employing a new curriculum learning strategy based on data cartography. Our dataset and code to reproduce the baselines are available at https://github.com/Eduard6421/RONLI. | [
"Poesina, Eduard",
"Caragea, Cornelia",
"Ionescu, Radu"
] | A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus | acl-long.15 | Poster | 2405.11877 | [
"https://github.com/eduard6421/ronli"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.15/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.16.bib | @inproceedings{chen-etal-2024-minprompt,
title = "{M}in{P}rompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering",
author = "Chen, Xiusi and
Jiang, Jyun-Yu and
Chang, Wei-Cheng and
Hsieh, Cho-Jui and
Yu, Hsiang-Fu and
Wang, Wei",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.16",
pages = "254--266",
abstract = "Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-training stage has already equipped LLMs with powerful reasoning capabilities, LLMs still need to be fine-tuned to adapt to specific domains to achieve the best results. In this paper, we propose to select the most informative data for fine-tuning, thereby improving the efficiency of the fine-tuning process with comparative or even better accuracy on the open-domain QA task. We present MinPrompt, a minimal data augmentation framework for open-domain QA based on an approximate graph algorithm and unsupervised question generation. We transform the raw text into a graph structure to build connections between different factual sentences, then apply graph algorithms to identify the minimal set of sentences needed to cover the most information in the raw text. We then generate QA pairs based on the identified sentence subset and train the model on the selected sentences to obtain the final model. Empirical results on several benchmark datasets and theoretical analysis show that MinPrompt is able to achieve comparable or better results than baselines with a high degree of efficiency, bringing consistent improvements in F-1 scores.",
}
| Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-training stage has already equipped LLMs with powerful reasoning capabilities, LLMs still need to be fine-tuned to adapt to specific domains to achieve the best results. In this paper, we propose to select the most informative data for fine-tuning, thereby improving the efficiency of the fine-tuning process with comparative or even better accuracy on the open-domain QA task. We present MinPrompt, a minimal data augmentation framework for open-domain QA based on an approximate graph algorithm and unsupervised question generation. We transform the raw text into a graph structure to build connections between different factual sentences, then apply graph algorithms to identify the minimal set of sentences needed to cover the most information in the raw text. We then generate QA pairs based on the identified sentence subset and train the model on the selected sentences to obtain the final model. Empirical results on several benchmark datasets and theoretical analysis show that MinPrompt is able to achieve comparable or better results than baselines with a high degree of efficiency, bringing consistent improvements in F-1 scores. | [
"Chen, Xiusi",
"Jiang, Jyun-Yu",
"Chang, Wei-Cheng",
"Hsieh, Cho-Jui",
"Yu, Hsiang-Fu",
"Wang, Wei"
] | MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering | acl-long.16 | Poster | 2310.05007 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.16/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.17.bib | @inproceedings{hu-etal-2024-sportsmetrics,
title = "{S}ports{M}etrics: Blending Text and Numerical Data to Understand Information Fusion in {LLM}s",
author = "Hu, Yebowen and
Song, Kaiqiang and
Cho, Sangwoo and
Wang, Xiaoyang and
Foroosh, Hassan and
Yu, Dong and
Liu, Fei",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.17",
pages = "267--278",
abstract = "Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents substantial challenges. LLMs need to process and cross-reference entities and numbers, handle data inconsistencies and redundancies, and develop planning capabilities such as building a working memory for managing complex data queries. In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs. These tasks involve providing LLMs with detailed, play-by-play sports game descriptions, then challenging them with adversarial scenarios such as new game rules, longer durations, scrambled narratives, and analyzing key statistics in game summaries. We conduct extensive experiments on NBA and NFL games to assess the performance of LLMs on these tasks. Our benchmark, SportsMetrics, introduces a new mechanism for assessing LLMs{'} numerical reasoning and fusion skills.",
}
| Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents substantial challenges. LLMs need to process and cross-reference entities and numbers, handle data inconsistencies and redundancies, and develop planning capabilities such as building a working memory for managing complex data queries. In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs. These tasks involve providing LLMs with detailed, play-by-play sports game descriptions, then challenging them with adversarial scenarios such as new game rules, longer durations, scrambled narratives, and analyzing key statistics in game summaries. We conduct extensive experiments on NBA and NFL games to assess the performance of LLMs on these tasks. Our benchmark, SportsMetrics, introduces a new mechanism for assessing LLMs{'} numerical reasoning and fusion skills. | [
"Hu, Yebowen",
"Song, Kaiqiang",
"Cho, Sangwoo",
"Wang, Xiaoyang",
"Foroosh, Hassan",
"Yu, Dong",
"Liu, Fei"
] | SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs | acl-long.17 | Poster | 2402.10979 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.17/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.18.bib | @inproceedings{wang-etal-2024-scimon,
title = "{S}ci{MON}: Scientific Inspiration Machines Optimized for Novelty",
author = "Wang, Qingyun and
Downey, Doug and
Ji, Heng and
Hope, Tom",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.18",
pages = "279--299",
abstract = "We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction{---}severely limiting the expressivity of hypotheses. This line of work also does not focus on optimizing novelty. We take a dramatic departure with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natural language ideas grounded in literature. We present SciMON, a modeling framework that uses retrieval of {``}inspirations{''} from past scientific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and updating idea suggestions until sufficient novelty is achieved. Comprehensive evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our methods partially mitigate this issue. Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature. Code, data, and resources are publicly available for research purposes: https://github.com/eaglew/clbd.",
}
| We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction{---}severely limiting the expressivity of hypotheses. This line of work also does not focus on optimizing novelty. We take a dramatic departure with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natural language ideas grounded in literature. We present SciMON, a modeling framework that uses retrieval of {``}inspirations{''} from past scientific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and updating idea suggestions until sufficient novelty is achieved. Comprehensive evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our methods partially mitigate this issue. Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature. Code, data, and resources are publicly available for research purposes: https://github.com/eaglew/clbd. | [
"Wang, Qingyun",
"Downey, Doug",
"Ji, Heng",
"Hope, Tom"
] | SciMON: Scientific Inspiration Machines Optimized for Novelty | acl-long.18 | Poster | 2305.14259 | [
"https://github.com/eaglew/clbd"
] | https://huggingface.co/papers/2305.14259 | 1 | 1 | 0 | 4 | https://aclanthology.org/2024.acl-long.18/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.19.bib | @inproceedings{jian-etal-2024-expedited,
title = "Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction",
author = "Jian, Yiren and
Liu, Tingkai and
Tao, Yunzhe and
Zhang, Chunhui and
Vosoughi, Soroush and
Yang, Hongxia",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.19",
pages = "300--314",
abstract = "We introduce $\text{EVL}_{\text{Gen}}$, a streamlined framework designed for the pre-training of visually conditioned language generation models with high computational demands, utilizing frozen pre-trained large language models (LLMs). The conventional approach in vision-language pre-training (VLP) typically involves a two-stage optimization process: an initial resource-intensive phase dedicated to general-purpose vision-language representation learning, focused on extracting and consolidating relevant visual features. This is followed by a subsequent phase that emphasizes end-to-end alignment between visual and linguistic modalities. Our novel one-stage, single-loss framework bypasses the computationally demanding first training stage by gradually merging similar visual tokens during training, while avoiding model collapse caused by single-stage training of BLIP-2 type models. The gradual merging process effectively condenses visual information while preserving semantic richness, resulting in rapid convergence without compromising performance. Our experimental findings demonstrate that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance. Furthermore, we illustrate that our models significantly narrow the performance gap to current vision-language models using only 1/10 of the data. Finally, we showcase how our image-text models can seamlessly adapt to video-conditioned language generation tasks through novel soft attentive temporal token contextualizing modules. Code: https://github.com/yiren-jian/EVLGen",
}
| We introduce $\text{EVL}_{\text{Gen}}$, a streamlined framework designed for the pre-training of visually conditioned language generation models with high computational demands, utilizing frozen pre-trained large language models (LLMs). The conventional approach in vision-language pre-training (VLP) typically involves a two-stage optimization process: an initial resource-intensive phase dedicated to general-purpose vision-language representation learning, focused on extracting and consolidating relevant visual features. This is followed by a subsequent phase that emphasizes end-to-end alignment between visual and linguistic modalities. Our novel one-stage, single-loss framework bypasses the computationally demanding first training stage by gradually merging similar visual tokens during training, while avoiding model collapse caused by single-stage training of BLIP-2 type models. The gradual merging process effectively condenses visual information while preserving semantic richness, resulting in rapid convergence without compromising performance. Our experimental findings demonstrate that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance. Furthermore, we illustrate that our models significantly narrow the performance gap to current vision-language models using only 1/10 of the data. Finally, we showcase how our image-text models can seamlessly adapt to video-conditioned language generation tasks through novel soft attentive temporal token contextualizing modules. Code: https://github.com/yiren-jian/EVLGen | [
"Jian, Yiren",
"Liu, Tingkai",
"Tao, Yunzhe",
"Zhang, Chunhui",
"Vosoughi, Soroush",
"Yang, Hongxia"
] | Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction | acl-long.19 | Oral | 2310.03291 | [
"https://github.com/yiren-jian/evlgen"
] | https://huggingface.co/papers/2310.03291 | 0 | 1 | 0 | 5 | https://aclanthology.org/2024.acl-long.19/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.20.bib | @inproceedings{kumar-etal-2024-confidence,
title = "Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models",
author = "Kumar, Abhishek and
Morabito, Robert and
Umbet, Sanzhar and
Kabbara, Jad and
Emami, Ali",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.20",
pages = "315--334",
abstract = "As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an LLM{'}s internal confidence, quantified by token probabilities, to the confidence conveyed in the model{'}s response when explicitly asked about its certainty. Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models{'} internal and expressed confidence. These techniques encompass using structured evaluation scales to rate confidence, including answer options when prompting, and eliciting the model{'}s confidence level for outputs it does not recognize as its own. Notably, among the models analyzed, OpenAI{'}s GPT-4 showed the strongest confidence-probability alignment, with an average Spearman{'}s $\hat{\rho}$ of 0.42, across a wide range of tasks. Our work contributes to the ongoing efforts to facilitate risk assessment in the application of LLMs and to further our understanding of model trustworthiness.",
}
| As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an LLM{'}s internal confidence, quantified by token probabilities, to the confidence conveyed in the model{'}s response when explicitly asked about its certainty. Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models{'} internal and expressed confidence. These techniques encompass using structured evaluation scales to rate confidence, including answer options when prompting, and eliciting the model{'}s confidence level for outputs it does not recognize as its own. Notably, among the models analyzed, OpenAI{'}s GPT-4 showed the strongest confidence-probability alignment, with an average Spearman{'}s $\hat{\rho}$ of 0.42, across a wide range of tasks. Our work contributes to the ongoing efforts to facilitate risk assessment in the application of LLMs and to further our understanding of model trustworthiness. | [
"Kumar, Abhishek",
"Morabito, Robert",
"Umbet, Sanzhar",
"Kabbara, Jad",
"Emami, Ali"
] | Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models | acl-long.20 | Poster | 2405.16282 | [
"https://github.com/akkeshav/confidence_probability_alignment"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.20/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.21.bib | @inproceedings{wang-etal-2024-retrieval,
title = "Retrieval-Augmented Multilingual Knowledge Editing",
author = "Wang, Weixuan and
Haddow, Barry and
Birch, Alexandra",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.21",
pages = "335--354",
abstract = "Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resource-hungry and not reliable, and so knowledge editing (KE) has developed as an effective and economical alternative to inject new knowledge or to fix factual errors in LLMs. Although there has been considerable interest in this area, current KE research exclusively focuses on monolingual settings, typically in English. However, what happens if the new knowledge is supplied in one language, but we would like to query an LLM in a different language? To address the problem of multilingual knowledge editing, we propose Retrieval-Augmented Multilingual Knowledge Editor (ReMaKE) to update knowledge in LLMs. ReMaKE can be used to perform model-agnostic knowledge editing in a multilingual setting. ReMaKE concatenates the new knowledge retrieved from a multilingual knowledge base with users{'} prompts before querying an LLM. Our experimental results show that ReMaKE outperforms baseline knowledge editing methods by a significant margin and is scalable to real-word application scenarios. Our multilingual knowledge editing dataset (MzsRE) in 12 languages, the code, and additional project information are available at https://github.com/weixuan-wang123/ReMaKE.",
}
| Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resource-hungry and not reliable, and so knowledge editing (KE) has developed as an effective and economical alternative to inject new knowledge or to fix factual errors in LLMs. Although there has been considerable interest in this area, current KE research exclusively focuses on monolingual settings, typically in English. However, what happens if the new knowledge is supplied in one language, but we would like to query an LLM in a different language? To address the problem of multilingual knowledge editing, we propose Retrieval-Augmented Multilingual Knowledge Editor (ReMaKE) to update knowledge in LLMs. ReMaKE can be used to perform model-agnostic knowledge editing in a multilingual setting. ReMaKE concatenates the new knowledge retrieved from a multilingual knowledge base with users{'} prompts before querying an LLM. Our experimental results show that ReMaKE outperforms baseline knowledge editing methods by a significant margin and is scalable to real-word application scenarios. Our multilingual knowledge editing dataset (MzsRE) in 12 languages, the code, and additional project information are available at https://github.com/weixuan-wang123/ReMaKE. | [
"Wang, Weixuan",
"Haddow, Barry",
"Birch, Alex",
"ra"
] | Retrieval-Augmented Multilingual Knowledge Editing | acl-long.21 | Poster | 2312.13040 | [
"https://github.com/vicky-wil/remake"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.21/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.22.bib | @inproceedings{park-etal-2024-picturing,
title = "Picturing Ambiguity: A Visual Twist on the {W}inograd Schema Challenge",
author = "Park, Brendan and
Janecek, Madeline and
Ezzati-Jivan, Naser and
Li, Yifeng and
Emami, Ali",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.22",
pages = "355--374",
abstract = "Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To address this, we introduce WinoVis, a novel dataset specifically designed to probe text-to-image models on pronoun disambiguation within multimodal contexts. Utilizing GPT-4 for prompt generation and Diffusion Attentive Attribution Maps (DAAM) for heatmap analysis, we propose a novel evaluation framework that isolates the models{'} ability in pronoun disambiguation from other visual processing challenges. Evaluation of successive model versions reveals that, despite incremental advancements, Stable Diffusion 2.0 achieves a precision of 56.7{\%} on WinoVis, only marginally surpassing random guessing. Further error analysis identifies important areas for future research aimed at advancing text-to-image models in their ability to interpret and interact with the complex visual world.",
}
| Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To address this, we introduce WinoVis, a novel dataset specifically designed to probe text-to-image models on pronoun disambiguation within multimodal contexts. Utilizing GPT-4 for prompt generation and Diffusion Attentive Attribution Maps (DAAM) for heatmap analysis, we propose a novel evaluation framework that isolates the models{'} ability in pronoun disambiguation from other visual processing challenges. Evaluation of successive model versions reveals that, despite incremental advancements, Stable Diffusion 2.0 achieves a precision of 56.7{\%} on WinoVis, only marginally surpassing random guessing. Further error analysis identifies important areas for future research aimed at advancing text-to-image models in their ability to interpret and interact with the complex visual world. | [
"Park, Brendan",
"Janecek, Madeline",
"Ezzati-Jivan, Naser",
"Li, Yifeng",
"Emami, Ali"
] | Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge | acl-long.22 | Oral | 2405.16277 | [
"https://github.com/bpark2/winovis"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.22/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.23.bib | @inproceedings{kumar-etal-2024-subtle,
title = "Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models",
author = "Kumar, Abhishek and
Yunusov, Sarfaroz and
Emami, Ali",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.23",
pages = "375--392",
abstract = "Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models{'} outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to generate outputs that mirror the experiences of certain identity groups, and affinity bias, reflecting the models{'} evaluative preferences for specific narratives or viewpoints. We introduce two novel metrics to measure these biases: the Representative Bias Score (RBS) and the Affinity Bias Score (ABS), and present the Creativity-Oriented Generation Suite (CoGS), a collection of open-ended tasks such as short story writing and poetry composition, designed with customized rubrics to detect these subtle biases. Our analysis uncovers marked representative biases in prominent LLMs, with a preference for identities associated with being white, straight, and men. Furthermore, our investigation of affinity bias reveals distinctive evaluative patterns within each model, akin to {`}bias fingerprints{'}. This trend is also seen in human evaluators, highlighting a complex interplay between human and machine bias perceptions.",
}
| Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models{'} outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to generate outputs that mirror the experiences of certain identity groups, and affinity bias, reflecting the models{'} evaluative preferences for specific narratives or viewpoints. We introduce two novel metrics to measure these biases: the Representative Bias Score (RBS) and the Affinity Bias Score (ABS), and present the Creativity-Oriented Generation Suite (CoGS), a collection of open-ended tasks such as short story writing and poetry composition, designed with customized rubrics to detect these subtle biases. Our analysis uncovers marked representative biases in prominent LLMs, with a preference for identities associated with being white, straight, and men. Furthermore, our investigation of affinity bias reveals distinctive evaluative patterns within each model, akin to {`}bias fingerprints{'}. This trend is also seen in human evaluators, highlighting a complex interplay between human and machine bias perceptions. | [
"Kumar, Abhishek",
"Yunusov, Sarfaroz",
"Emami, Ali"
] | Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models | acl-long.23 | Poster | 2405.14555 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.23/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.24.bib | @inproceedings{leto-etal-2024-framing,
title = "Framing in the Presence of Supporting Data: A Case Study in {U}.{S}. Economic News",
author = "Leto, Alexandria and
Pickens, Elliot and
Needell, Coen and
Rothschild, David and
Pacheco, Maria",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.24",
pages = "393--415",
abstract = "The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics where objective measures exist in the form of supporting data and propose a computational framework to analyze editorial choices in this setup. We focus on the economy because the reporting of economic indicators presents us with a relatively easy way to determine both the selection and framing of various publications. Their values provide a ground truth of how the economy is doing relative to how the publications choose to cover it. To do this, we define frame prediction as a set of interdependent tasks. At the article level, we learn to identify the reported stance towards the general state of the economy. Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way. To perform our analysis, we track six American publishers and each article that appeared in the top 10 slots of their landing page between 2015 and 2023.",
}
| The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics where objective measures exist in the form of supporting data and propose a computational framework to analyze editorial choices in this setup. We focus on the economy because the reporting of economic indicators presents us with a relatively easy way to determine both the selection and framing of various publications. Their values provide a ground truth of how the economy is doing relative to how the publications choose to cover it. To do this, we define frame prediction as a set of interdependent tasks. At the article level, we learn to identify the reported stance towards the general state of the economy. Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way. To perform our analysis, we track six American publishers and each article that appeared in the top 10 slots of their landing page between 2015 and 2023. | [
"Leto, Alex",
"ria",
"Pickens, Elliot",
"Needell, Coen",
"Rothschild, David",
"Pacheco, Maria"
] | Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News | acl-long.24 | Poster | 2402.14224 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.24/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.25.bib | @inproceedings{wang-etal-2024-mementos,
title = "Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences",
author = "Wang, Xiyao and
Zhou, Yuhang and
Liu, Xiaoyu and
Lu, Hongjin and
Xu, Yuancheng and
He, Feihong and
Yoon, Jaehong and
Lu, Taixi and
Liu, Fuxiao and
Bertasius, Gedas and
Bansal, Mohit and
Yao, Huaxiu and
Huang, Furong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.25",
pages = "416--442",
abstract = "Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated. To address this challenge, this paper introduces Mementos, a new benchmark designed to assess MLLMs{'} sequential image reasoning abilities. Mementos features 4,761 diverse image sequences with varying lengths. We also employ a GPT-4 assisted method to evaluate MLLM reasoning performance. Through a careful evaluation of nine recent MLLMs on Mementos, including GPT-4V and Gemini, we find that they struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects and their corresponding behaviors. Our quantitative analysis and case studies identify three key factors impacting MLLMs{'} sequential image reasoning: the correlation between object and behavioral hallucinations, the influence of co-occurring behaviors, and the compounding impact of behavioral hallucinations.",
}
| Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated. To address this challenge, this paper introduces Mementos, a new benchmark designed to assess MLLMs{'} sequential image reasoning abilities. Mementos features 4,761 diverse image sequences with varying lengths. We also employ a GPT-4 assisted method to evaluate MLLM reasoning performance. Through a careful evaluation of nine recent MLLMs on Mementos, including GPT-4V and Gemini, we find that they struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects and their corresponding behaviors. Our quantitative analysis and case studies identify three key factors impacting MLLMs{'} sequential image reasoning: the correlation between object and behavioral hallucinations, the influence of co-occurring behaviors, and the compounding impact of behavioral hallucinations. | [
"Wang, Xiyao",
"Zhou, Yuhang",
"Liu, Xiaoyu",
"Lu, Hongjin",
"Xu, Yuancheng",
"He, Feihong",
"Yoon, Jaehong",
"Lu, Taixi",
"Liu, Fuxiao",
"Bertasius, Gedas",
"Bansal, Mohit",
"Yao, Huaxiu",
"Huang, Furong"
] | Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences | acl-long.25 | Poster | 2401.10529 | [
"https://github.com/umd-huang-lab/mementos"
] | https://huggingface.co/papers/2401.10529 | 4 | 1 | 0 | 12 | https://aclanthology.org/2024.acl-long.25/ | [] | [
"furonghuang-lab/Mementos"
] | [] | 1 |
https://aclanthology.org/2024.acl-long.26.bib | @inproceedings{gao-etal-2024-ttm,
title = "{TTM}-{RE}: Memory-Augmented Document-Level Relation Extraction",
author = "Gao, Chufan and
Wang, Xuan and
Sun, Jimeng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.26",
pages = "443--458",
abstract = "Document-level relation extraction aims to categorize the association between any two entities within a document.We find that previous methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. For example, in the ReDocRED benchmark dataset, state-of-the-art methods trained on the large-scale, lower-quality, distantly supervised training data generally do not perform better than those trained solely on the smaller, high-quality, human-annotated training data. To unlock the full potential of large-scale noisy training data for document-level relation extraction, we propose TTM-RE, a novel approach that integrates a trainable memory module, known as the Token Turing Machine, with a noisy-robust loss function that accounts for the positive-unlabeled setting. The trainable memory module enhances knowledge extraction from the large-scale noisy training dataset through an explicit learning of the memory tokens and a soft integration of the learned memory tokens into the input representation, thereby improving the model{'}s effectiveness for the final relation classification. Extensive experiments on ReDocRED, a benchmark dataset for document-level relation extraction, reveal that TTM-RE achieves state-of-the-art performance (with an absolute F1 score improvement of over 3{\%}). Ablation studies further illustrate the superiority of TTM-RE in other domains (the ChemDisGene dataset in the biomedical domain) and under highly unlabeled settings.",
}
| Document-level relation extraction aims to categorize the association between any two entities within a document.We find that previous methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. For example, in the ReDocRED benchmark dataset, state-of-the-art methods trained on the large-scale, lower-quality, distantly supervised training data generally do not perform better than those trained solely on the smaller, high-quality, human-annotated training data. To unlock the full potential of large-scale noisy training data for document-level relation extraction, we propose TTM-RE, a novel approach that integrates a trainable memory module, known as the Token Turing Machine, with a noisy-robust loss function that accounts for the positive-unlabeled setting. The trainable memory module enhances knowledge extraction from the large-scale noisy training dataset through an explicit learning of the memory tokens and a soft integration of the learned memory tokens into the input representation, thereby improving the model{'}s effectiveness for the final relation classification. Extensive experiments on ReDocRED, a benchmark dataset for document-level relation extraction, reveal that TTM-RE achieves state-of-the-art performance (with an absolute F1 score improvement of over 3{\%}). Ablation studies further illustrate the superiority of TTM-RE in other domains (the ChemDisGene dataset in the biomedical domain) and under highly unlabeled settings. | [
"Gao, Chufan",
"Wang, Xuan",
"Sun, Jimeng"
] | TTM-RE: Memory-Augmented Document-Level Relation Extraction | acl-long.26 | Poster | 2406.05906 | [
"https://github.com/chufangao/ttm-re"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.26/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.27.bib | @inproceedings{peng-etal-2024-answer,
title = "Answer is All You Need: Instruction-following Text Embedding via Answering the Question",
author = "Peng, Letian and
Zhang, Yuwei and
Wang, Zilong and
Srinivasa, Jayanth and
Liu, Gaowen and
Wang, Zihan and
Shang, Jingbo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.27",
pages = "459--477",
abstract = "This work aims to build a text embedder that can capture characteristics of texts specified by user instructions clarifying the similarity criterion. While previous methods improve general task awareness by injecting the instruction information into encoding, they fail to be sensitive to clearer criteria like {``}evaluate similarity based on emotion{''}. We instead propose a different viewpoint, which treats the instruction as a {``}question{''} about the input text and encodes the expected answers to obtain the representation accordingly. Intuitively, texts with the same (implicit) semantics would share similar answers following the instruction, thus leading to more similar representations. Specifically, we propose InBedder that instantiates this learning-to-answer idea by only fine-tuning language models via abstractive question answering tasks. Despite its simplicity, InBedder demonstrates significantly improved instruction-following capabilities according to our proposed instruction awareness tests and instruction robustness tests, when applied to language models with large language models (LLMs) (e.g., llama-2-7b) and smaller encoder-based LMs (e.g., roberta-large). Additionally, our qualitative analysis of clustering outcomes, achieved by applying diverse instructions to the same unlabeled corpus, demonstrates a high degree of interpretability in the clusters formed.",
}
| This work aims to build a text embedder that can capture characteristics of texts specified by user instructions clarifying the similarity criterion. While previous methods improve general task awareness by injecting the instruction information into encoding, they fail to be sensitive to clearer criteria like {``}evaluate similarity based on emotion{''}. We instead propose a different viewpoint, which treats the instruction as a {``}question{''} about the input text and encodes the expected answers to obtain the representation accordingly. Intuitively, texts with the same (implicit) semantics would share similar answers following the instruction, thus leading to more similar representations. Specifically, we propose InBedder that instantiates this learning-to-answer idea by only fine-tuning language models via abstractive question answering tasks. Despite its simplicity, InBedder demonstrates significantly improved instruction-following capabilities according to our proposed instruction awareness tests and instruction robustness tests, when applied to language models with large language models (LLMs) (e.g., llama-2-7b) and smaller encoder-based LMs (e.g., roberta-large). Additionally, our qualitative analysis of clustering outcomes, achieved by applying diverse instructions to the same unlabeled corpus, demonstrates a high degree of interpretability in the clusters formed. | [
"Peng, Letian",
"Zhang, Yuwei",
"Wang, Zilong",
"Srinivasa, Jayanth",
"Liu, Gaowen",
"Wang, Zihan",
"Shang, Jingbo"
] | Answer is All You Need: Instruction-following Text Embedding via Answering the Question | acl-long.27 | Poster | 2402.09642 | [
"https://github.com/zhang-yu-wei/inbedder"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.27/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.28.bib | @inproceedings{zhou-etal-2024-explore,
title = "Explore Spurious Correlations at the Concept Level in Language Models for Text Classification",
author = "Zhou, Yuhang and
Xu, Paiheng and
Liu, Xiaoyu and
An, Bang and
Ai, Wei and
Huang, Furong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.28",
pages = "478--492",
abstract = "Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distributions in training data or ICL exemplars. Previous research has primarily concentrated on word, phrase, and syntax features, neglecting the concept level, often due to the absence of concept labels and difficulty in identifying conceptual content in input texts. This paper introduces two main contributions. First, we employ ChatGPT to assign concept labels to texts, assessing concept bias in models during fine-tuning or ICL on test data. We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions. Second, we introduce a data rebalancing technique that incorporates ChatGPT-generated counterfactual data, thereby balancing label distribution and mitigating spurious correlations. Our method{'}s efficacy, surpassing traditional token removal approaches, is validated through extensive testing.",
}
| Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distributions in training data or ICL exemplars. Previous research has primarily concentrated on word, phrase, and syntax features, neglecting the concept level, often due to the absence of concept labels and difficulty in identifying conceptual content in input texts. This paper introduces two main contributions. First, we employ ChatGPT to assign concept labels to texts, assessing concept bias in models during fine-tuning or ICL on test data. We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions. Second, we introduce a data rebalancing technique that incorporates ChatGPT-generated counterfactual data, thereby balancing label distribution and mitigating spurious correlations. Our method{'}s efficacy, surpassing traditional token removal approaches, is validated through extensive testing. | [
"Zhou, Yuhang",
"Xu, Paiheng",
"Liu, Xiaoyu",
"An, Bang",
"Ai, Wei",
"Huang, Furong"
] | Explore Spurious Correlations at the Concept Level in Language Models for Text Classification | acl-long.28 | Poster | 2311.08648 | [
"https://github.com/tonyzhou98/concept-spurious-correlation"
] | https://huggingface.co/papers/2311.08648 | 1 | 2 | 0 | 6 | https://aclanthology.org/2024.acl-long.28/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.29.bib | @inproceedings{cheng-etal-2024-every,
title = "Every Answer Matters: Evaluating Commonsense with Probabilistic Measures",
author = "Cheng, Qi and
Boratko, Michael and
Yelugam, Pranay Kumar and
O{'}Gorman, Tim and
Singh, Nalini and
McCallum, Andrew and
Li, Xiang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.29",
pages = "493--506",
abstract = "Large language models have demonstrated impressive performance on commonsense tasks; however, these tasks are often posed as multiple-choice questions, allowing models to exploit systematic biases. Commonsense is also inherently probabilistic with multiple correct answers. The purpose of {``}boiling water{''} could be making tea, cooking but also could be killing germs. Existing tasks do not capture the probabilistic nature of common sense. To this end, we present commonsense frame completion (CFC), a new generative task that evaluates common sense via multiple open-ended generations. We also propose a method of probabilistic evaluation that strongly correlates with human judgments. Humans drastically outperform strong language model baselines on our dataset, indicating this approach is both a challenging and useful evaluation of machine common sense.",
}
| Large language models have demonstrated impressive performance on commonsense tasks; however, these tasks are often posed as multiple-choice questions, allowing models to exploit systematic biases. Commonsense is also inherently probabilistic with multiple correct answers. The purpose of {``}boiling water{''} could be making tea, cooking but also could be killing germs. Existing tasks do not capture the probabilistic nature of common sense. To this end, we present commonsense frame completion (CFC), a new generative task that evaluates common sense via multiple open-ended generations. We also propose a method of probabilistic evaluation that strongly correlates with human judgments. Humans drastically outperform strong language model baselines on our dataset, indicating this approach is both a challenging and useful evaluation of machine common sense. | [
"Cheng, Qi",
"Boratko, Michael",
"Yelugam, Pranay Kumar",
"O{'}Gorman, Tim",
"Singh, Nalini",
"McCallum, Andrew",
"Li, Xiang"
] | Every Answer Matters: Evaluating Commonsense with Probabilistic Measures | acl-long.29 | Poster | 2406.04145 | [
"https://github.com/qxc101/probeval_cfc"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.29/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.30.bib | @inproceedings{xie-etal-2024-gradsafe,
title = "{G}rad{S}afe: Detecting Jailbreak Prompts for {LLM}s via Safety-Critical Gradient Analysis",
author = "Xie, Yueqi and
Fang, Minghong and
Pi, Renjie and
Gong, Neil",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.30",
pages = "507--518",
abstract = "Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These strategies, however, often require extensive and resource-intensive data collection and training processes. In this study, we propose GradSafe, which effectively detects jailbreak prompts by scrutinizing the gradients of safety-critical parameters in LLMs. Our method is grounded in a pivotal observation: the gradients of an LLM{'}s loss for jailbreak prompts paired with compliance response exhibit similar patterns on certain safety-critical parameters. In contrast, safe prompts lead to different gradient patterns. Building on this observation, GradSafe analyzes the gradients from prompts (paired with compliance responses) to accurately detect jailbreak prompts. We show that GradSafe, applied to Llama-2 without further training, outperforms Llama Guard{---}despite its extensive finetuning with a large dataset{---}in detecting jailbreak prompts. This superior performance is consistent across both zero-shot and adaptation scenarios, as evidenced by our evaluations on ToxicChat and XSTest. The source code is available at https://github.com/xyq7/GradSafe.",
}
| Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These strategies, however, often require extensive and resource-intensive data collection and training processes. In this study, we propose GradSafe, which effectively detects jailbreak prompts by scrutinizing the gradients of safety-critical parameters in LLMs. Our method is grounded in a pivotal observation: the gradients of an LLM{'}s loss for jailbreak prompts paired with compliance response exhibit similar patterns on certain safety-critical parameters. In contrast, safe prompts lead to different gradient patterns. Building on this observation, GradSafe analyzes the gradients from prompts (paired with compliance responses) to accurately detect jailbreak prompts. We show that GradSafe, applied to Llama-2 without further training, outperforms Llama Guard{---}despite its extensive finetuning with a large dataset{---}in detecting jailbreak prompts. This superior performance is consistent across both zero-shot and adaptation scenarios, as evidenced by our evaluations on ToxicChat and XSTest. The source code is available at https://github.com/xyq7/GradSafe. | [
"Xie, Yueqi",
"Fang, Minghong",
"Pi, Renjie",
"Gong, Neil"
] | GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis | acl-long.30 | Poster | 2402.13494 | [
"https://github.com/xyq7/gradsafe"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.30/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.31.bib | @inproceedings{lee-etal-2024-pouring,
title = "Pouring Your Heart Out: Investigating the Role of Figurative Language in Online Expressions of Empathy",
author = "Lee, Gyeongeun and
Wong, Christina and
Guo, Meghan and
Parde, Natalie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.31",
pages = "519--529",
abstract = "Empathy is a social mechanism used to support and strengthen emotional connection with others, including in online communities. However, little is currently known about the nature of these online expressions, nor the particular factors that may lead to their improved detection. In this work, we study the role of a specific and complex subcategory of linguistic phenomena, figurative language, in online expressions of empathy. Our extensive experiments reveal that incorporating features regarding the use of metaphor, idiom, and hyperbole into empathy detection models improves their performance, resulting in impressive maximum F1 scores of 0.942 and 0.809 for identifying posts without and with empathy, respectively.",
}
| Empathy is a social mechanism used to support and strengthen emotional connection with others, including in online communities. However, little is currently known about the nature of these online expressions, nor the particular factors that may lead to their improved detection. In this work, we study the role of a specific and complex subcategory of linguistic phenomena, figurative language, in online expressions of empathy. Our extensive experiments reveal that incorporating features regarding the use of metaphor, idiom, and hyperbole into empathy detection models improves their performance, resulting in impressive maximum F1 scores of 0.942 and 0.809 for identifying posts without and with empathy, respectively. | [
"Lee, Gyeongeun",
"Wong, Christina",
"Guo, Meghan",
"Parde, Natalie"
] | Pouring Your Heart Out: Investigating the Role of Figurative Language in Online Expressions of Empathy | acl-long.31 | Poster | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.31/ | [] | [] | [] | 0 |
||
https://aclanthology.org/2024.acl-long.32.bib | @inproceedings{wang-etal-2024-information,
title = "An Information-Theoretic Approach to Analyze {NLP} Classification Tasks",
author = "Wang, Luran and
Gales, Mark and
Raina, Vatsal",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.32",
pages = "530--551",
abstract = "Understanding the contribution of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single or multiple text elements to predict an output variable. Each text element has two components: the semantic meaning and a linguistic realization. Multiple-choice reading comprehension (MCRC) and sentiment classification (SC) are selected to showcase the framework. For MCRC, it is found that the relative context influence on the output reduces on more challenging datasets. In particular, more challenging contexts allows greater variation in the question complexity. Hence, test creators need to carefully consider the choice of the context when designing multiple-choice questions for assessment. For SC, it is found the semantic meaning of the input dominates compared to its linguistic realization when determining the sentiment. The framework is made available at: https://github.com/WangLuran/nlp-element-influence.",
}
| Understanding the contribution of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single or multiple text elements to predict an output variable. Each text element has two components: the semantic meaning and a linguistic realization. Multiple-choice reading comprehension (MCRC) and sentiment classification (SC) are selected to showcase the framework. For MCRC, it is found that the relative context influence on the output reduces on more challenging datasets. In particular, more challenging contexts allows greater variation in the question complexity. Hence, test creators need to carefully consider the choice of the context when designing multiple-choice questions for assessment. For SC, it is found the semantic meaning of the input dominates compared to its linguistic realization when determining the sentiment. The framework is made available at: https://github.com/WangLuran/nlp-element-influence. | [
"Wang, Luran",
"Gales, Mark",
"Raina, Vatsal"
] | An Information-Theoretic Approach to Analyze NLP Classification Tasks | acl-long.32 | Poster | 2402.00978 | [
"https://github.com/wangluran/nlp-element-influence"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.32/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.33.bib | @inproceedings{zhang-etal-2024-model,
title = "Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders",
author = "Zhang, Yuwei and
Singh, Siffi and
Sengupta, Sailik and
Shalyminov, Igor and
Su, Hang and
Song, Hwanjun and
Mansour, Saab",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.33",
pages = "552--567",
abstract = "Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semantics over the embedding space using prompts, are being viewed as a panacea for these downstream conversational tasks. However, traditional evaluation benchmarks rely solely on task metrics that don{'}t particularly measure gaps related to semantic understanding. Thus, we propose an intent semantic toolkit that gives a more holistic view of intent embedding models by considering three tasks{--} (1) intent classification, (2) intent clustering, and (3) a novel triplet task. The triplet task gauges the model{'}s understanding of two semantic concepts paramount in real-world conversational systems{--} negation and implicature. We observe that current embedding models fare poorly in semantic understanding of these concepts. To address this, we propose a pre-training approach to improve the embedding model by leveraging augmentation with data generated by an auto-regressive model and a contrastive loss term. Our approach improves the semantic understanding of the intent embedding model on the aforementioned linguistic dimensions while slightly effecting their performance on downstream task metrics.",
}
| Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semantics over the embedding space using prompts, are being viewed as a panacea for these downstream conversational tasks. However, traditional evaluation benchmarks rely solely on task metrics that don{'}t particularly measure gaps related to semantic understanding. Thus, we propose an intent semantic toolkit that gives a more holistic view of intent embedding models by considering three tasks{--} (1) intent classification, (2) intent clustering, and (3) a novel triplet task. The triplet task gauges the model{'}s understanding of two semantic concepts paramount in real-world conversational systems{--} negation and implicature. We observe that current embedding models fare poorly in semantic understanding of these concepts. To address this, we propose a pre-training approach to improve the embedding model by leveraging augmentation with data generated by an auto-regressive model and a contrastive loss term. Our approach improves the semantic understanding of the intent embedding model on the aforementioned linguistic dimensions while slightly effecting their performance on downstream task metrics. | [
"Zhang, Yuwei",
"Singh, Siffi",
"Sengupta, Sailik",
"Shalyminov, Igor",
"Su, Hang",
"Song, Hwanjun",
"Mansour, Saab"
] | Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders | acl-long.33 | Poster | 2403.04314 | [
""
] | https://huggingface.co/papers/2403.04314 | 0 | 0 | 0 | 7 | https://aclanthology.org/2024.acl-long.33/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.34.bib | @inproceedings{he-etal-2024-wav2gloss,
title = "{W}av2{G}loss: Generating Interlinear Glossed Text from Speech",
author = "He, Taiqi and
Choi, Kwanghee and
Tjuatja, Lindia and
Robinson, Nathaniel and
Shi, Jiatong and
Watanabe, Shinji and
Neubig, Graham and
Mortensen, David and
Levin, Lori",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.34",
pages = "568--582",
abstract = "Thousands of the world{'}s languages are in danger of extinction{---}a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages{'} communities. IGT typically consists of (1) transcriptions, (2) morphological segmentation, (3) glosses, and (4) free translations to a majority language. We propose Wav2Gloss: a task in which these four annotation components are extracted automatically from speech, and introduce the first dataset to this end, Fieldwork: a corpus of speech with all these annotations, derived from the work of field linguists, covering 37 languages, with standard formatting, and train/dev/test splits. We provide various baselines to lay the groundwork for future research on IGT generation from speech, such as end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multi-task approaches.",
}
| Thousands of the world{'}s languages are in danger of extinction{---}a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages{'} communities. IGT typically consists of (1) transcriptions, (2) morphological segmentation, (3) glosses, and (4) free translations to a majority language. We propose Wav2Gloss: a task in which these four annotation components are extracted automatically from speech, and introduce the first dataset to this end, Fieldwork: a corpus of speech with all these annotations, derived from the work of field linguists, covering 37 languages, with standard formatting, and train/dev/test splits. We provide various baselines to lay the groundwork for future research on IGT generation from speech, such as end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multi-task approaches. | [
"He, Taiqi",
"Choi, Kwanghee",
"Tjuatja, Lindia",
"Robinson, Nathaniel",
"Shi, Jiatong",
"Watanabe, Shinji",
"Neubig, Graham",
"Mortensen, David",
"Levin, Lori"
] | Wav2Gloss: Generating Interlinear Glossed Text from Speech | acl-long.34 | Poster | 2403.13169 | [
""
] | https://huggingface.co/papers/2403.13169 | 0 | 0 | 0 | 9 | https://aclanthology.org/2024.acl-long.34/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.35.bib | @inproceedings{hu-etal-2024-leveraging,
title = "Leveraging Codebook Knowledge with {NLI} and {C}hat{GPT} for Zero-Shot Political Relation Classification",
author = "Hu, Yibo and
Skorupa Parolin, Erick and
Khan, Latifur and
Brandt, Patrick and
Osorio, Javier and
D{'}Orazio, Vito",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.35",
pages = "583--603",
abstract = "Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation codebook, and evaluates the performance of advanced ChatGPT (GPT-3.5/4) and a natural language inference (NLI)-based model called ZSP. ChatGPT uses codebook{'}s labeled summaries as prompts, whereas ZSP breaks down the classification task into context, event mode, and class disambiguation to refine task-specific hypotheses. This decomposition enhances interpretability, efficiency, and adaptability to schema changes. The experiments reveal ChatGPT{'}s strengths and limitations, and crucially show ZSP{'}s outperformance of dictionary-based methods and its competitive edge over some supervised models. These findings affirm the value of ZSP for validating event records and advancing ontology development. Our study underscores the efficacy of leveraging transfer learning and existing domain expertise to enhance research efficiency and scalability.",
}
| Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation codebook, and evaluates the performance of advanced ChatGPT (GPT-3.5/4) and a natural language inference (NLI)-based model called ZSP. ChatGPT uses codebook{'}s labeled summaries as prompts, whereas ZSP breaks down the classification task into context, event mode, and class disambiguation to refine task-specific hypotheses. This decomposition enhances interpretability, efficiency, and adaptability to schema changes. The experiments reveal ChatGPT{'}s strengths and limitations, and crucially show ZSP{'}s outperformance of dictionary-based methods and its competitive edge over some supervised models. These findings affirm the value of ZSP for validating event records and advancing ontology development. Our study underscores the efficacy of leveraging transfer learning and existing domain expertise to enhance research efficiency and scalability. | [
"Hu, Yibo",
"Skorupa Parolin, Erick",
"Khan, Latifur",
"Br",
"t, Patrick",
"Osorio, Javier",
"D{'}Orazio, Vito"
] | Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification | acl-long.35 | Poster | 2308.07876 | [
"https://github.com/snowood1/zero-shot-plover"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.35/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.36.bib | @inproceedings{xu-wang-2024-spor,
title = "{SPOR}: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation",
author = "Xu, Ziyao and
Wang, Houfeng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.36",
pages = "604--621",
abstract = "Compositional generalization is an important ability of language models and has many different manifestations. For data-to-text generation, previous research on this ability is limited to a single manifestation called Systematicity and lacks consideration of large language models (LLMs), which cannot fully cover practical application scenarios. In this work, we propose SPOR, a comprehensive and practical evaluation method for compositional generalization in data-to-text generation. SPOR includes four aspects of manifestations (Systematicity, Productivity, Order invariance, and Rule learnability) and allows high-quality evaluation without additional manual annotations based on existing datasets. We demonstrate SPOR on two different datasets and evaluate some existing language models including LLMs. We find that the models are deficient in various aspects of the evaluation and need further improvement. Our work shows the necessity for comprehensive research on different manifestations of compositional generalization in data-to-text generation and provides a framework for evaluation.",
}
| Compositional generalization is an important ability of language models and has many different manifestations. For data-to-text generation, previous research on this ability is limited to a single manifestation called Systematicity and lacks consideration of large language models (LLMs), which cannot fully cover practical application scenarios. In this work, we propose SPOR, a comprehensive and practical evaluation method for compositional generalization in data-to-text generation. SPOR includes four aspects of manifestations (Systematicity, Productivity, Order invariance, and Rule learnability) and allows high-quality evaluation without additional manual annotations based on existing datasets. We demonstrate SPOR on two different datasets and evaluate some existing language models including LLMs. We find that the models are deficient in various aspects of the evaluation and need further improvement. Our work shows the necessity for comprehensive research on different manifestations of compositional generalization in data-to-text generation and provides a framework for evaluation. | [
"Xu, Ziyao",
"Wang, Houfeng"
] | SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation | acl-long.36 | Poster | 2405.10650 | [
"https://github.com/xzy-xzy/spor"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.36/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.37.bib | @inproceedings{shi-etal-2024-opex,
title = "{OPE}x: A Component-Wise Analysis of {LLM}-Centric Agents in Embodied Instruction Following",
author = "Shi, Haochen and
Sun, Zhiyuan and
Yuan, Xingdi and
C{\^o}t{\'e}, Marc-Alexandre and
Liu, Bang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.37",
pages = "622--636",
abstract = "Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learning tasks, including EIF. Despite these efforts, there exists a lack of a unified understanding regarding the impact of various components{---}ranging from visual perception to action execution{---}on task performance. To address this gap, we introduce OPEx, a comprehensive framework that delineates the core components essential for solving embodied learning tasks: Observer, Planner, and Executor. Through extensive evaluations, we provide a deep analysis of how each component influences EIF task performance. Furthermore, we innovate within this space by integrating a multi-agent design into the Planner component of our LLM-centric architecture, further enhancing task performance. Our findings reveal that LLM-centric design markedly improves EIF outcomes, identify visual perception and low-level action execution as critical bottlenecks, and demonstrate that augmenting LLMs with a multi-agent framework further elevates performance.",
}
| Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learning tasks, including EIF. Despite these efforts, there exists a lack of a unified understanding regarding the impact of various components{---}ranging from visual perception to action execution{---}on task performance. To address this gap, we introduce OPEx, a comprehensive framework that delineates the core components essential for solving embodied learning tasks: Observer, Planner, and Executor. Through extensive evaluations, we provide a deep analysis of how each component influences EIF task performance. Furthermore, we innovate within this space by integrating a multi-agent design into the Planner component of our LLM-centric architecture, further enhancing task performance. Our findings reveal that LLM-centric design markedly improves EIF outcomes, identify visual perception and low-level action execution as critical bottlenecks, and demonstrate that augmenting LLMs with a multi-agent framework further elevates performance. | [
"Shi, Haochen",
"Sun, Zhiyuan",
"Yuan, Xingdi",
"C{\\^o}t{\\'e}, Marc-Alex",
"re",
"Liu, Bang"
] | OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following | acl-long.37 | Poster | 2403.03017 | [
""
] | https://huggingface.co/papers/2403.03017 | 1 | 0 | 0 | 5 | https://aclanthology.org/2024.acl-long.37/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.38.bib | @inproceedings{shen-etal-2024-multimodal,
title = "Multimodal Instruction Tuning with Conditional Mixture of {L}o{RA}",
author = "Shen, Ying and
Xu, Zhiyang and
Wang, Qifan and
Cheng, Yu and
Yin, Wenpeng and
Huang, Lifu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.38",
pages = "637--648",
abstract = "Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks. Multimodal instruction tuning has emerged as a successful strategy for achieving zero-shot generalization by fine-tuning pre-trained models on diverse multimodal tasks through instructions. As MLLMs grow in complexity and size, the need for parameter-efficient fine-tuning methods like Low-Rank Adaption (LoRA), which fine-tunes with a minimal set of parameters, becomes essential. However, applying LoRA in multimodal instruction tuning presents the challenge of task interference, which leads to performance degradation, especially when dealing with a broad array of multimodal tasks. To address this, this paper introduces a novel approach that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA (MixLoRA). It innovates upon LoRA by dynamically constructing low-rank adaptation matrices tailored to the unique demands of each input instance, aiming to mitigate task interference. Experimental results on various multimodal evaluation datasets indicate that MixLoRA not only outperforms the conventional LoRA with the same or even higher ranks, demonstrating its efficacy and adaptability in diverse multimodal tasks.",
}
| Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks. Multimodal instruction tuning has emerged as a successful strategy for achieving zero-shot generalization by fine-tuning pre-trained models on diverse multimodal tasks through instructions. As MLLMs grow in complexity and size, the need for parameter-efficient fine-tuning methods like Low-Rank Adaption (LoRA), which fine-tunes with a minimal set of parameters, becomes essential. However, applying LoRA in multimodal instruction tuning presents the challenge of task interference, which leads to performance degradation, especially when dealing with a broad array of multimodal tasks. To address this, this paper introduces a novel approach that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA (MixLoRA). It innovates upon LoRA by dynamically constructing low-rank adaptation matrices tailored to the unique demands of each input instance, aiming to mitigate task interference. Experimental results on various multimodal evaluation datasets indicate that MixLoRA not only outperforms the conventional LoRA with the same or even higher ranks, demonstrating its efficacy and adaptability in diverse multimodal tasks. | [
"Shen, Ying",
"Xu, Zhiyang",
"Wang, Qifan",
"Cheng, Yu",
"Yin, Wenpeng",
"Huang, Lifu"
] | Multimodal Instruction Tuning with Conditional Mixture of LoRA | acl-long.38 | Poster | 2402.15896 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.38/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.39.bib | @inproceedings{xie-etal-2024-doclens,
title = "{D}oc{L}ens: Multi-aspect Fine-grained Medical Text Evaluation",
author = "Xie, Yiqing and
Zhang, Sheng and
Cheng, Hao and
Liu, Pengfei and
Gero, Zelalem and
Wong, Cliff and
Naumann, Tristan and
Poon, Hoifung and
Rose, Carolyn",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.39",
pages = "649--679",
abstract = "Medical text generation aims to assist with administrative work and highlight salient information to support decision-making.To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completeness, conciseness, and attribution of the generated text at a fine-grained level. The metrics can be computed by various types of evaluators including instruction-following (both proprietary and open-source) and supervised entailment models. We demonstrate the effectiveness of the resulting framework, DocLens, with three evaluators on three tasks: clinical note generation, radiology report summarization, and patient question summarization. A comprehensive human study shows that DocLens exhibits substantially higher agreement with the judgments of medical experts than existing metrics. The results also highlight the need to improve open-source evaluators and suggest potential directions. We released the code at https://github.com/yiqingxyq/DocLens.",
}
| Medical text generation aims to assist with administrative work and highlight salient information to support decision-making.To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completeness, conciseness, and attribution of the generated text at a fine-grained level. The metrics can be computed by various types of evaluators including instruction-following (both proprietary and open-source) and supervised entailment models. We demonstrate the effectiveness of the resulting framework, DocLens, with three evaluators on three tasks: clinical note generation, radiology report summarization, and patient question summarization. A comprehensive human study shows that DocLens exhibits substantially higher agreement with the judgments of medical experts than existing metrics. The results also highlight the need to improve open-source evaluators and suggest potential directions. We released the code at https://github.com/yiqingxyq/DocLens. | [
"Xie, Yiqing",
"Zhang, Sheng",
"Cheng, Hao",
"Liu, Pengfei",
"Gero, Zelalem",
"Wong, Cliff",
"Naumann, Tristan",
"Poon, Hoifung",
"Rose, Carolyn"
] | DocLens: Multi-aspect Fine-grained Medical Text Evaluation | acl-long.39 | Poster | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.39/ | [] | [] | [] | 0 |
||
https://aclanthology.org/2024.acl-long.40.bib | @inproceedings{xia-etal-2024-fofo,
title = "{FOFO}: A Benchmark to Evaluate {LLM}s{'} Format-Following Capability",
author = "Xia, Congying and
Xing, Chen and
Du, Jiangshu and
Yang, Xinyi and
Feng, Yihao and
Xu, Ran and
Yin, Wenpeng and
Xiong, Caiming",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.40",
pages = "680--699",
abstract = "This paper presents FoFo, a pioneering benchmark for evaluating large language models{'} (LLMs) ability to follow complex, domain-specific formats, a crucial yet under-examined capability for their application as AI agents. Despite LLMs{'} advancements, existing benchmarks fail to assess their format-following proficiency adequately. FoFo fills this gap with a diverse range of real-world formats and instructions, developed through an AI-Human collaborative method. Our evaluation across both open-source (e.g., Llama 2, WizardLM) and closed-source (e.g., GPT-4, PALM2, Gemini) LLMs highlights three key findings: open-source models significantly lag behind closed-source ones in format adherence; LLMs{'} format-following performance is independent of their content generation quality; and LLMs{'} format proficiency varies across different domains. These insights suggest the need for specialized tuning for format-following skills and highlight FoFo{'}s role in guiding the selection of domain-specific AI agents. FoFo will be publicly released, contributing a critical tool for advancing LLM evaluation and application.",
}
| This paper presents FoFo, a pioneering benchmark for evaluating large language models{'} (LLMs) ability to follow complex, domain-specific formats, a crucial yet under-examined capability for their application as AI agents. Despite LLMs{'} advancements, existing benchmarks fail to assess their format-following proficiency adequately. FoFo fills this gap with a diverse range of real-world formats and instructions, developed through an AI-Human collaborative method. Our evaluation across both open-source (e.g., Llama 2, WizardLM) and closed-source (e.g., GPT-4, PALM2, Gemini) LLMs highlights three key findings: open-source models significantly lag behind closed-source ones in format adherence; LLMs{'} format-following performance is independent of their content generation quality; and LLMs{'} format proficiency varies across different domains. These insights suggest the need for specialized tuning for format-following skills and highlight FoFo{'}s role in guiding the selection of domain-specific AI agents. FoFo will be publicly released, contributing a critical tool for advancing LLM evaluation and application. | [
"Xia, Congying",
"Xing, Chen",
"Du, Jiangshu",
"Yang, Xinyi",
"Feng, Yihao",
"Xu, Ran",
"Yin, Wenpeng",
"Xiong, Caiming"
] | FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability | acl-long.40 | Poster | 2402.18667 | [
"https://github.com/salesforceairesearch/fofo"
] | https://huggingface.co/papers/2402.18667 | 0 | 0 | 0 | 8 | https://aclanthology.org/2024.acl-long.40/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.41.bib | @inproceedings{yoo-etal-2024-hyper,
title = "Hyper-{CL}: Conditioning Sentence Representations with Hypernetworks",
author = "Yoo, Young and
Cha, Jii and
Kim, Changhyeon and
Kim, Taeuk",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.41",
pages = "700--711",
abstract = "While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives.In this paper, we introduce Hyper-CL, an efficient methodology that integrates hypernetworks with contrastive learning to compute conditioned sentence representations.In our proposed approach, the hypernetwork is responsible for transforming pre-computed condition embeddings into corresponding projection layers. This enables the same sentence embeddings to be projected differently according to various conditions.Evaluation on two representative conditioning benchmarks, namely conditional semantic text similarity and knowledge graph completion, demonstrates that Hyper-CL is effective in flexibly conditioning sentence representations, showcasing its computational efficiency at the same time.We also provide a comprehensive analysis of the inner workings of our approach, leading to a better interpretation of its mechanisms.",
}
| While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives.In this paper, we introduce Hyper-CL, an efficient methodology that integrates hypernetworks with contrastive learning to compute conditioned sentence representations.In our proposed approach, the hypernetwork is responsible for transforming pre-computed condition embeddings into corresponding projection layers. This enables the same sentence embeddings to be projected differently according to various conditions.Evaluation on two representative conditioning benchmarks, namely conditional semantic text similarity and knowledge graph completion, demonstrates that Hyper-CL is effective in flexibly conditioning sentence representations, showcasing its computational efficiency at the same time.We also provide a comprehensive analysis of the inner workings of our approach, leading to a better interpretation of its mechanisms. | [
"Yoo, Young",
"Cha, Jii",
"Kim, Changhyeon",
"Kim, Taeuk"
] | Hyper-CL: Conditioning Sentence Representations with Hypernetworks | acl-long.41 | Poster | 2403.09490 | [
"https://github.com/hyu-nlp/hyper-cl"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.41/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.42.bib | @inproceedings{lim-etal-2024-analysis,
title = "Analysis of Multi-Source Language Training in Cross-Lingual Transfer",
author = "Lim, Seonghoon and
Yun, Taejun and
Kim, Jinhyeon and
Choi, Jihun and
Kim, Taeuk",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.42",
pages = "712--725",
abstract = "The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, there still exists ongoing debate about the mechanisms behind their effectiveness.In this work, we focus on one of promising assumptions about inner workings of XLT, that it encourages multilingual LMs to place greater emphasis on language-agnostic or task-specific features. We test this hypothesis by examining how the patterns of XLT change with a varying number of source languages involved in the process.Our experimental findings show that the use of multiple source languages in XLT-a technique we term Multi-Source Language Training (MSLT)-leads to increased mingling of embedding spaces for different languages, supporting the claim that XLT benefits from making use of language-independent information. On the other hand, we discover that using an arbitrary combination of source languages does not always guarantee better performance. We suggest simple heuristics for identifying effective language combinations for MSLT and empirically prove its effectiveness.",
}
| The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, there still exists ongoing debate about the mechanisms behind their effectiveness.In this work, we focus on one of promising assumptions about inner workings of XLT, that it encourages multilingual LMs to place greater emphasis on language-agnostic or task-specific features. We test this hypothesis by examining how the patterns of XLT change with a varying number of source languages involved in the process.Our experimental findings show that the use of multiple source languages in XLT-a technique we term Multi-Source Language Training (MSLT)-leads to increased mingling of embedding spaces for different languages, supporting the claim that XLT benefits from making use of language-independent information. On the other hand, we discover that using an arbitrary combination of source languages does not always guarantee better performance. We suggest simple heuristics for identifying effective language combinations for MSLT and empirically prove its effectiveness. | [
"Lim, Seonghoon",
"Yun, Taejun",
"Kim, Jinhyeon",
"Choi, Jihun",
"Kim, Taeuk"
] | Analysis of Multi-Source Language Training in Cross-Lingual Transfer | acl-long.42 | Poster | 2402.13562 | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.42/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.43.bib | @inproceedings{ghosh-etal-2024-abex,
title = "{ABEX}: Data Augmentation for Low-Resource {NLU} via Expanding Abstract Descriptions",
author = "Ghosh, Sreyan and
Tyagi, Utkarsh and
Kumar, Sonal and
Evuru, Chandra Kiran and
S, Ramaneswaran and
Sakshi, S and
Manocha, Dinesh",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.43",
pages = "726--748",
abstract = "We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document {--} we first convert a document into its concise, abstract description and then generate new documents based on expanding the resultant abstraction. To learn the task of expanding abstract descriptions, we first train BART on a large-scale synthetic dataset with abstract-document pairs. Next, to generate abstract descriptions for a document, we propose a simple, controllable, and training-free method based on editing AMR graphs. ABEX brings the best of both worlds: by expanding from abstract representations, it preserves the original semantic properties of the documents, like style and meaning, thereby maintaining alignment with the original label and data distribution. At the same time, the fundamental process of elaborating on abstract descriptions facilitates diverse generations. We demonstrate the effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource settings. ABEX outperforms all our baselines qualitatively with improvements of 0.04{\%} - 38.8{\%}. Qualitatively, ABEX outperforms all prior methods from literature in terms of context and length diversity.",
}
| We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document {--} we first convert a document into its concise, abstract description and then generate new documents based on expanding the resultant abstraction. To learn the task of expanding abstract descriptions, we first train BART on a large-scale synthetic dataset with abstract-document pairs. Next, to generate abstract descriptions for a document, we propose a simple, controllable, and training-free method based on editing AMR graphs. ABEX brings the best of both worlds: by expanding from abstract representations, it preserves the original semantic properties of the documents, like style and meaning, thereby maintaining alignment with the original label and data distribution. At the same time, the fundamental process of elaborating on abstract descriptions facilitates diverse generations. We demonstrate the effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource settings. ABEX outperforms all our baselines qualitatively with improvements of 0.04{\%} - 38.8{\%}. Qualitatively, ABEX outperforms all prior methods from literature in terms of context and length diversity. | [
"Ghosh, Sreyan",
"Tyagi, Utkarsh",
"Kumar, Sonal",
"Evuru, Ch",
"ra Kiran",
"S, Ramaneswaran",
"Sakshi, S",
"Manocha, Dinesh"
] | ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions | acl-long.43 | Poster | 2406.04286 | [
"https://github.com/sreyan88/abex"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.43/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.44.bib | @inproceedings{bandarkar-etal-2024-belebele,
title = "The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants",
author = "Bandarkar, Lucas and
Liang, Davis and
Muller, Benjamin and
Artetxe, Mikel and
Shukla, Satya Narayan and
Husa, Donald and
Goyal, Naman and
Krishnan, Abhinandan and
Zettlemoyer, Luke and
Khabsa, Madian",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.44",
pages = "749--775",
abstract = "We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the FLORES-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and findings, notably that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems.",
}
| We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the FLORES-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and findings, notably that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems. | [
"B",
"arkar, Lucas",
"Liang, Davis",
"Muller, Benjamin",
"Artetxe, Mikel",
"Shukla, Satya Narayan",
"Husa, Donald",
"Goyal, Naman",
"Krishnan, Abhin",
"an",
"Zettlemoyer, Luke",
"Khabsa, Madian"
] | The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants | acl-long.44 | Poster | 2308.16884 | [
"https://github.com/facebookresearch/belebele"
] | https://huggingface.co/papers/2308.16884 | 4 | 8 | 0 | 10 | https://aclanthology.org/2024.acl-long.44/ | [
"ilsp/Meltemi-7B-v1",
"ilsp/Meltemi-7B-Instruct-v1",
"HiTZ/latxa-7b-v1",
"ilsp/Meltemi-7B-Instruct-v1.5",
"HiTZ/latxa-70b-v1",
"HiTZ/latxa-13b-v1",
"ilsp/Meltemi-7B-v1.5",
"HiTZ/latxa-7b-v1.1",
"SPAHE/Meltemi-7B-Instruct-v1-GGUF",
"HiTZ/latxa-13b-v1.1",
"HiTZ/latxa-70b-v1.1",
"HiTZ/latxa-13b-v1.2",
"HiTZ/latxa-7b-v1.2",
"HiTZ/latxa-70b-v1.2",
"RichardErkhov/ilsp_-_Meltemi-7B-Instruct-v1.5-gguf"
] | [
"SEACrowd/belebele",
"OALL/AlGhafa-Arabic-LLM-Benchmark-Native"
] | [
"teketen/idiazabal",
"SantiagoMoreno-UdeA/Latxa-demo"
] | 1 |
https://aclanthology.org/2024.acl-long.45.bib | @inproceedings{an-etal-2024-learn,
title = "Learn from Failure: Fine-tuning {LLM}s with Trial-and-Error Data for Intuitionistic Propositional Logic Proving",
author = "An, Chenyang and
Chen, Zhibo and
Ye, Qihao and
First, Emily and
Peng, Letian and
Zhang, Jiayun and
Wang, Zihan and
Lerner, Sorin and
Shang, Jingbo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.45",
pages = "776--790",
abstract = "Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and try various tactics at each proof state until finding success, unlike its training which does not incorporate learning from failed attempts. Intuitively, a tactic that leads to a failed search path would indicate that similar tactics should receive less attention during the following trials. In this paper, we demonstrate the benefit of training models that additionally learn from failed search paths. Facing the lack of such trial-and-error data in existing open-source theorem-proving datasets, we curate a dataset on intuitionistic propositional logic theorems and formalize it in Lean, such that we can reliably check the correctness of proofs. We compare our model trained on relatively short trial-and-error information (TrialMaster) with models trained only on the correct paths and discover that the former solves more unseen theorems with lower trial searches.",
}
| Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and try various tactics at each proof state until finding success, unlike its training which does not incorporate learning from failed attempts. Intuitively, a tactic that leads to a failed search path would indicate that similar tactics should receive less attention during the following trials. In this paper, we demonstrate the benefit of training models that additionally learn from failed search paths. Facing the lack of such trial-and-error data in existing open-source theorem-proving datasets, we curate a dataset on intuitionistic propositional logic theorems and formalize it in Lean, such that we can reliably check the correctness of proofs. We compare our model trained on relatively short trial-and-error information (TrialMaster) with models trained only on the correct paths and discover that the former solves more unseen theorems with lower trial searches. | [
"An, Chenyang",
"Chen, Zhibo",
"Ye, Qihao",
"First, Emily",
"Peng, Letian",
"Zhang, Jiayun",
"Wang, Zihan",
"Lerner, Sorin",
"Shang, Jingbo"
] | Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving | acl-long.45 | Poster | 2404.07382 | [
"https://github.com/ucsd-atp/propl"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.45/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.46.bib | @inproceedings{lee-etal-2024-interactive,
title = "Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach",
author = "Lee, Saehyung and
Yu, Sangwon and
Park, Junsung and
Yi, Jihun and
Yoon, Sungroh",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.46",
pages = "791--809",
abstract = "In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the general instruction-following capability of LLMs in two ways. First, by reformulating the dialogue-form context, we eliminate the necessity of fine-tuning a retrieval model on existing visual dialogue data, thereby enabling the use of any arbitrary black-box model. Second, we construct the LLM questioner to generate non-redundant questions about the attributes of the target image, based on the information of retrieval candidate images in the current context. This approach mitigates the issues of noisiness and redundancy in the generated questions. Beyond our methodology, we propose a novel evaluation metric, Best log Rank Integral (BRI), for a comprehensive assessment of the interactive retrieval system. PlugIR demonstrates superior performance compared to both zero-shot and fine-tuned baselines in various benchmarks. Additionally, the two methodologies comprising PlugIR can be flexibly applied together or separately in various situations.",
}
| In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the general instruction-following capability of LLMs in two ways. First, by reformulating the dialogue-form context, we eliminate the necessity of fine-tuning a retrieval model on existing visual dialogue data, thereby enabling the use of any arbitrary black-box model. Second, we construct the LLM questioner to generate non-redundant questions about the attributes of the target image, based on the information of retrieval candidate images in the current context. This approach mitigates the issues of noisiness and redundancy in the generated questions. Beyond our methodology, we propose a novel evaluation metric, Best log Rank Integral (BRI), for a comprehensive assessment of the interactive retrieval system. PlugIR demonstrates superior performance compared to both zero-shot and fine-tuned baselines in various benchmarks. Additionally, the two methodologies comprising PlugIR can be flexibly applied together or separately in various situations. | [
"Lee, Saehyung",
"Yu, Sangwon",
"Park, Junsung",
"Yi, Jihun",
"Yoon, Sungroh"
] | Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach | acl-long.46 | Oral | 2406.03411 | [
"https://github.com/saehyung-lee/plugir"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.46/ | [] | [] | [] | 0 |
|
https://aclanthology.org/2024.acl-long.47.bib | @inproceedings{lin-etal-2024-imbue,
title = "{IMBUE}: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction",
author = "Lin, Inna and
Sharma, Ashish and
Rytting, Christopher and
Miner, Adam and
Suh, Jina and
Althoff, Tim",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.47",
pages = "810--840",
abstract = "Navigating certain communication situations can be challenging due to individuals{'} lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training and provide just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. We apply the interpersonal effectiveness framework from Dialectical Behavioral Therapy (DBT), DEAR MAN, which focuses on both conversational and emotional skills. We present IMBUE, an interactive training system that provides feedback 28{\%} more similar to experts{'} feedback, compared to that generated by GPT-4. IMBUE is the first to focus on communication skills and emotion management simultaneously, incorporate experts{'} domain knowledge in providing feedback, and be grounded in psychology theory. Through a randomized trial of 86 participants, we find that IMBUE{'}s simulation-only variant significantly improves participants{'} self-efficacy (up to 17{\%}) and reduces negative emotions (up to 25{\%}). With IMBUE{'}s additional just-in-time feedback, participants demonstrate 17{\%} improvement in skill mastery, along with greater enhancements in self-efficacy (27{\%} more) and reduction of negative emotions (16{\%} more) compared to simulation-only. The improvement in skill mastery is the only measure that is transferred to new and more difficult situations; situation-specific training is necessary for improving self-efficacy and emotion reduction.",
}
| Navigating certain communication situations can be challenging due to individuals{'} lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training and provide just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. We apply the interpersonal effectiveness framework from Dialectical Behavioral Therapy (DBT), DEAR MAN, which focuses on both conversational and emotional skills. We present IMBUE, an interactive training system that provides feedback 28{\%} more similar to experts{'} feedback, compared to that generated by GPT-4. IMBUE is the first to focus on communication skills and emotion management simultaneously, incorporate experts{'} domain knowledge in providing feedback, and be grounded in psychology theory. Through a randomized trial of 86 participants, we find that IMBUE{'}s simulation-only variant significantly improves participants{'} self-efficacy (up to 17{\%}) and reduces negative emotions (up to 25{\%}). With IMBUE{'}s additional just-in-time feedback, participants demonstrate 17{\%} improvement in skill mastery, along with greater enhancements in self-efficacy (27{\%} more) and reduction of negative emotions (16{\%} more) compared to simulation-only. The improvement in skill mastery is the only measure that is transferred to new and more difficult situations; situation-specific training is necessary for improving self-efficacy and emotion reduction. | [
"Lin, Inna",
"Sharma, Ashish",
"Rytting, Christopher",
"Miner, Adam",
"Suh, Jina",
"Althoff, Tim"
] | IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction | acl-long.47 | Poster | 2402.12556 | [
""
] | https://huggingface.co/papers/2402.12556 | 0 | 0 | 0 | 6 | https://aclanthology.org/2024.acl-long.47/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.48.bib | @inproceedings{lin-etal-2024-token,
title = "Token-wise Influential Training Data Retrieval for Large Language Models",
author = "Lin, Huawei and
Long, Jikai and
Xu, Zhaozhuo and
Zhao, Weijie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.48",
pages = "841--860",
abstract = "Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we compress the gradient vectors by over 200,000x, allowing them to be cached on disk or in GPU/CPU memory. Then, given a generation, RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup. Moreover, RapidIn supports multi-GPU parallelization to substantially accelerate caching and retrieval. Our empirical result confirms the efficiency and effectiveness of RapidIn.",
}
| Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we compress the gradient vectors by over 200,000x, allowing them to be cached on disk or in GPU/CPU memory. Then, given a generation, RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup. Moreover, RapidIn supports multi-GPU parallelization to substantially accelerate caching and retrieval. Our empirical result confirms the efficiency and effectiveness of RapidIn. | [
"Lin, Huawei",
"Long, Jikai",
"Xu, Zhaozhuo",
"Zhao, Weijie"
] | Token-wise Influential Training Data Retrieval for Large Language Models | acl-long.48 | Poster | 2405.11724 | [
"https://github.com/huawei-lin/rapidin"
] | https://huggingface.co/papers/2405.11724 | 0 | 0 | 0 | 4 | https://aclanthology.org/2024.acl-long.48/ | [
"huaweilin/rapidin-alpaca-llama2-7b"
] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.49.bib | @inproceedings{weinzierl-harabagiu-2024-tree,
title = "Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection",
author = "Weinzierl, Maxwell and
Harabagiu, Sanda",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.49",
pages = "861--880",
abstract = "Stance detection enables the inference of attitudes from human communications. Automatic stance identification was mostly cast as a classification problem. However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs). In this paper we present a new method for stance identification which (1) relies on a new prompting framework, called Tree-of-Counterfactual prompting; (2) operates not only on textual communications, but also on images; (3) allows more than one stance object type; and (4) requires no examples of stance attribution, thus it is a {``}Tabula Rasa{''} Zero-Shot Stance Detection (TR-ZSSD) method. Our experiments indicate surprisingly promising results, outperforming fine-tuned stance detection systems.",
}
| Stance detection enables the inference of attitudes from human communications. Automatic stance identification was mostly cast as a classification problem. However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs). In this paper we present a new method for stance identification which (1) relies on a new prompting framework, called Tree-of-Counterfactual prompting; (2) operates not only on textual communications, but also on images; (3) allows more than one stance object type; and (4) requires no examples of stance attribution, thus it is a {``}Tabula Rasa{''} Zero-Shot Stance Detection (TR-ZSSD) method. Our experiments indicate surprisingly promising results, outperforming fine-tuned stance detection systems. | [
"Weinzierl, Maxwell",
"Harabagiu, S",
"a"
] | Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection | acl-long.49 | Poster | [
""
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.49/ | [] | [] | [] | 0 |
||
https://aclanthology.org/2024.acl-long.50.bib | @inproceedings{koh-etal-2024-visualwebarena,
title = "{V}isual{W}eb{A}rena: Evaluating Multimodal Agents on Realistic Visual Web Tasks",
author = "Koh, Jing Yu and
Lo, Robert and
Jang, Lawrence and
Duvvur, Vikram and
Lim, Ming and
Huang, Po-Yu and
Neubig, Graham and
Zhou, Shuyan and
Salakhutdinov, Russ and
Fried, Daniel",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.50",
pages = "881--905",
abstract = "Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on *realistic visually grounded tasks*. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web.",
}
| Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on *realistic visually grounded tasks*. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web. | [
"Koh, Jing Yu",
"Lo, Robert",
"Jang, Lawrence",
"Duvvur, Vikram",
"Lim, Ming",
"Huang, Po-Yu",
"Neubig, Graham",
"Zhou, Shuyan",
"Salakhutdinov, Russ",
"Fried, Daniel"
] | VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks | acl-long.50 | Poster | 2401.13649 | [
"https://github.com/web-arena-x/visualwebarena"
] | https://huggingface.co/papers/2401.13649 | 0 | 1 | 0 | 10 | https://aclanthology.org/2024.acl-long.50/ | [] | [] | [] | 1 |
https://aclanthology.org/2024.acl-long.51.bib | @inproceedings{song-etal-2024-finesure,
title = "{F}ine{S}ur{E}: Fine-grained Summarization Evaluation using {LLM}s",
author = "Song, Hwanjun and
Su, Hang and
Shalyminov, Igor and
Cai, Jason and
Mansour, Saab",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.51",
pages = "906--922",
abstract = "Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional methods like ROUGE do not correlate well with human judgment, while recently proposed LLM-based metrics provide only summary-level assessment using Likert-scale scores. This limits deeper model analysis, e.g., we can only assign one hallucination score at the summary level, while at the sentence level, we can count sentences containing hallucinations. To remedy those limitations, we propose FineSurE, a fine-grained evaluator specifically tailored for the summarization task using large language models (LLMs). It also employs completeness and conciseness criteria, in addition to faithfulness, enabling multi-dimensional assessment. We compare various open-source and proprietary LLMs as backbones for FineSurE. In addition, we conduct extensive benchmarking of FineSurE against SOTA methods including NLI-, QA-, and LLM-based methods, showing improved performance especially on the completeness and conciseness dimensions. The code is available at https://github.com/DISL-Lab/FineSurE.",
}
| Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional methods like ROUGE do not correlate well with human judgment, while recently proposed LLM-based metrics provide only summary-level assessment using Likert-scale scores. This limits deeper model analysis, e.g., we can only assign one hallucination score at the summary level, while at the sentence level, we can count sentences containing hallucinations. To remedy those limitations, we propose FineSurE, a fine-grained evaluator specifically tailored for the summarization task using large language models (LLMs). It also employs completeness and conciseness criteria, in addition to faithfulness, enabling multi-dimensional assessment. We compare various open-source and proprietary LLMs as backbones for FineSurE. In addition, we conduct extensive benchmarking of FineSurE against SOTA methods including NLI-, QA-, and LLM-based methods, showing improved performance especially on the completeness and conciseness dimensions. The code is available at https://github.com/DISL-Lab/FineSurE. | [
"Song, Hwanjun",
"Su, Hang",
"Shalyminov, Igor",
"Cai, Jason",
"Mansour, Saab"
] | FineSurE: Fine-grained Summarization Evaluation using LLMs | acl-long.51 | Poster | 2407.00908 | [
"https://github.com/disl-lab/finesure-acl24"
] | -1 | -1 | -1 | -1 | https://aclanthology.org/2024.acl-long.51/ | [] | [] | [] | 0 |
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